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With the development of distributed energy resources, peer-to-peer (P2P) energy trading has emerged for consuming locally produced energy. In the energy buildings, the end-users consume not only electricity. They also compete to purchase electricity, gas, and heat. Therefore, this paper promotes the conventional P2P energy market to a multi-energy scenario. We first propose integrated energy flexibility with energy storages to facilitate the end-users to trade energy flexibly. Considering their bidding characteristics, we then employ mental accounts and prospect theory to depict the prosumers' subjective decisions. To include individual bidding uncertainty in the trading results, we develop a distributionally robust chance-constrained (DRCC) market-clearing model. Finally, we exploit the surrogate building model based on a deep learning algorithm for building managers to bid in the building-to-building (B2B) energy market. We solve the inner-building DRCC market clearing problem with the alternating direction method of multipliers (ADMM) to protect the prosumers' privacy. Given the buildings' marginal costs in surrogate models, we clear the B2B energy market with continuous double auctions in a decentralized way. Moreover, we verify the convergence of market-clearing algorithms in numerical cases. Comparing the energy consumption costs in the multi-energy and pure electric cases demonstrates that the P2P energy market promotion is effective. • The prosumers' information characteristics are depicted. • Energy storages and hubs are employed to improve flexibility. • A surrogate building trading model is proposed based on a deep belief network. • A continuous double auction mechanism is developed to match buildings. [ABSTRACT FROM AUTHOR]

Space cooling of buildings using evaporative cooling is a passive strategy that can provide thermal comfort for occupants and conserve energy. In this paper, an innovative evaporative cooling wet media material in the form of synthetic grass fibre mat was introduced to evaluate experimentally its effectiveness in Direct Evaporative Cooling (DEC), Indirect Evaporative Cooling (IEC), and Dew point evaporative cooling (DPEC). A laboratory evaporative cooling module was constructed and tested using a fully instrumented test rig and was subject to environmentally controlled conditions of air temperature and relative humidity (RH) commensurate to those prevailing in hot and dry climates like the Middle East. The wet bulb effectiveness and cooling capacity of evaporative coolers equipped with the new wet media material were evaluated experimentally. The results show that these evaporative coolers performance correlate well with current technology. For instance, the ambient air temperature is reduced by as much as 12.9 °C, 10.2 °C, and 10.3 °C for direct, indirect, and dew point modes, respectively. It was also shown that DEC achieved effectiveness 20 % higher than indirect evaporative coolers. The work demonstrated that the new wet media can be a viable alternative to organic and non-organic counterparts. [ABSTRACT FROM AUTHOR]

• The zigzag Trombe wall, a novel combined Trombe wall, was proposed here; • A well-designed experiment was conducted on the Trombe walls in winter days; • Parameters influencing the zigzag Trombe wall thermal performance were investigated; • Transient fluid flow and heat transfer of the zigzag Trombe wall were disclosed; • Zigzag Trombe wall could be applied for such poor solar energy regions. Solar energy building applications are gaining more attention due to their benefits in sustainable development, including energy conservation, cost reduction, and environmental protection. Present work proposed the zigzag Trombe wall, and its performance was investigated by on-site reduced-scale experiments and delicate CFD simulations, on a typical winter day in Wuhan, China. Unlike the traditional Trombe walls, our proposed zigzag Trombe wall has two sections, one of the sections comprises a southeast-facing window that provides both natural light and direct heat gain in the morning when promotion of heating efficiency is most needed; whereas the other consists of a southwest-facing Trombe wall with circulating vents that stores the afternoon sun's heat for redistribution during the relatively cold night hours. Here, fluid flow, heat distribution, and thermal comfort were introduced as key indices to investigate the performance of the zigzag Trombe wall around the whole typical day, regarding of the effects of the angle α and area ratio γ between two sections. Simulation results of the zigzag Trombe wall were compared with that of the classical Trombe wall, and which showed that this new one could effectively distribute heat throughout the day, especially in the morning period. This paper provides theoretical support and optimization ideas for the design and operation of the novel zigzag Trombe wall through a combination of experimental and numerical simulation methods. [ABSTRACT FROM AUTHOR]

Natural building materials, including bioaggregates and biocomposites, have been making a comeback in recent years, in an attempt to minimize the Embodied and Operational Energy and Carbon (EE, OE, EC, OC) related to construction. This paper presents monitoring results of three buildings (two residential and one educational) built with such materials in the Mediterranean climate of western Crete. Temperature and relative humidity monitoring results show a very stable indoor environment behavior even when the buildings were unoccupied, thus not appropriately operated. Additional measurements undertaken and presented here include CO 2 concentration in the occupied residential building with a combustion heater; and blow-door tests to better understand infiltration issues related with such construction. Results analysis shows that such materials are appropriate alternatives, definitely in bioclimatic building design for the specific climate. Indoor conditions analyzed vis-à-vis the adaptive thermal comfort model are shown to be within the thermal comfort zone, almost for all climatic conditions. [ABSTRACT FROM AUTHOR]

• A survey was administered in QC, Canada to investigate participation in DR Programs. • Results characterized contextual factors that influence participation in DR. • Results identified significant potential to expand DR programs in the province. • Respondents believed "smart" home appliances can improve their participation in DR. • Results provided valuable insights to further develop DR programs in North America. The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province. [ABSTRACT FROM AUTHOR]

The research on nonintrusive load monitoring (NILM) typically focuses on residential and commercial users. However, high-energy industrial users also require load monitoring analysis to understand their electricity consumption patterns and load operating conditions. Currently used load monitoring methods and parameters are not applicable to industrial environments. In this paper, we propose an innovative multichannel low window sequence-to-point (MLSP) method based on deep neural networks for NILM of high-energy industrial users. First, the data from each channel of the main line terminal are normalized and then aggregated into a time series, which is several times longer than the original data to expand its data volume. Then, the multichannel low window sequence-to-point method is used to select the window data of several times the length, corresponding to the active power of the target device. Finally, the discriminative features of the data are learned using a deep neural network model. The analysis of actual industrial user data shows that the lowest mean absolute error (MAE) of the original sequence-to-point method for related branches is 20.38, and the highest correlation coefficient (CC) is 90.23%. Our proposed method reduces the MAE to 11.02 and increases the CC to 97.42%. The MAE and CC indicators of all branches decreased by 25.8% and increased by 22.4%, respectively. According to Mean Absolute Percentage Error (MAPE), the performance of this model is improved by 39.6% compared with Seq2point. Compared with the original sequence-to-point method, our proposed method achieves better load monitoring results and can more accurately decompose load fluctuations, which are closer to the actual load curve. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. An Automatic Fault Detection and Diagnosis (AFDD) tool could help facility managers fix comfort and energy issues more efficiently, by identifying the most probable root causes. Existing AFDD methods mostly focus on equipment-level fault detection and diagnostics; almost no attention is given to building level fault diagnosis, considering inter-dependency between equipment through the energy distribution chain. In this work we propose a methodology to automatically derive a Bayesian network from HVAC system topology description such as Haystack. This Bayesian network models and estimates the state of all elements in the system, helping users to identify the most probable root fault. As it is able to ingest evidence from any source (field data, operators, or other models) and is capable of updating its estimates when new evidence is delivered, such a tool could have a great potential to be used interactively on the field. We applied the proposed methodology on simulated and real-world buildings and present in this paper one specific case. • Transfer building system topology and expert knowledge to a Bayesian network. • Transform equipment-level fault detection into building-wide fault diagnosis. • Experiments on simulated and real-world buildings. [ABSTRACT FROM AUTHOR]

The Passive House (PH) Standard is a voluntary building energy performance standard focused upon reducing space heating demand to a very low level and therefore considered a viable climate change mitigation technology. Besides comfort and energy requirements, the PH standard also defines criteria with respect to ventilation. However, the question remains, how well do PH dwellings perform when they are occupied? Does the PH approach provide good indoor air quality (IAQ) for its occupants and how does IAQ compare to non-PH homes, in particular, naturally ventilated homes? Additionally, can PH certification improve the quality of installed ventilation systems? This paper summarizes indoor air quality relevant aspects of the PH standard and presents results from measurements examining in-use IAQ in more than 600 PH or PH-like, newly built or retrofitted dwellings. The results reveal that pollutant and carbon dioxide concentration are generally lower compared to naturally ventilated homes, presumably due to the requirement to install a balanced Mechanical Ventilation with Heat Recovery (MVHR) system. Results also suggest that the quality assurance measures of PH certification are capable of improving ventilation and IAQ performance. However, the lack of cooking fume capture requirements in the PH standard, in combination with efforts to avoid energy losses associated with a possible extraction kitchen hood, may lead to elevated particulate matter concentration in PHs. Future research on cooking induced IAQ impairment is encouraged to assess the effectiveness of recently published PH-specific recommendations. Future efforts in empirical IAQ research should also address the lack of high quality IAQ measurement data and the standardisation of IAQ assessment methods and protocols. [ABSTRACT FROM AUTHOR]

Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2 % to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

[Display omitted] • Early design decisions highly influence building's embodied emission performance. • Building Information Model support automated Life Cycle Assessments. • Semantic model healing ensure reliable results of Life Cycle Assessments. • Uncertainties in early stages lead to a range of LCA results. • Natural language processing support automation of matching LCA data to BIM elements. To reach the goals of limiting global warming, the embodied greenhouse gas (GHG) emissions of new buildings need to be quantified and optimized in the very early design stages, during which design decisions significantly influence the success of projects in achieving their performance goals. Semantically rich building information models (BIM) enable to perform an automated quantity take-off of the relevant elements for calculating a whole building life cycle assessment (LCA). However, imprecise type and property information often found in today's BIM practice hinders a seamless processing for downstream applications. At the same time, the early design stages are characterized by high uncertainty due to the lack of information and knowledge, making a holistic and consistent LCA for supporting design decisions and optimizing performance challenging. In assessing this often vague information, it is essential to consider different levels of element and material information for matching BIM to LCA data. For example, the structural properties of concrete are not yet defined in early design stages and should instead be considered as a range of material options due to different compressive strength classes. This paper presents a novel methodology for automatically matching the coarse information available in BIM models of the early design stages to the respective entries in LCA databases as a basis for a fully automated calculation process of the embodied GHG emissions of new buildings. This approach solves the existing gap in the automation process of manually enriching BIM models and adding information of LCA data and missing layers of vague models. In more detail, the proposed method is based on Natural Language Processing (NLP), using different strategies to increase performance in matching elements and materials from a BIM model to a knowledge database to enrich environmental indicators of commonly used elements' materials. The knowledge database contains all missing information for LCAs and has different levels of information for a range of several potential design options of elements and materials, including their dependencies. Accordingly, this paper investigates multiple NLP techniques and evaluates the performance of state-of-the-art deep learning models such as GermaNet, SpaCy, or BERT. Following this, the most performant NLP approach is used to provide an automatic workflow for matching Industry Foundation Classes (IFC) elements to the knowledge database, facilitating a seamless LCA in the early stages of design. For five different case studies, the performances of the proposed matching method are analyzed. Finally, one case study is selected to compare the embodied emissions results to those of the conventional process. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

To meet the goals established by the 2015 Paris Accord, scientists say that we will need to achieve zero emissions between 2030 and 2050, which will require a drastic departure from current practices in the building industry, as buildings are a primary contributor of carbon emissions and a major consumer of natural resources. However, building technology research is often driven by disciplinary 'experts' and end-users have little opportunity for involvement in the design process. As buildings become more technologically complex, users must rely on industry professionals with specialized knowledge to design, manufacture and manage these increasingly complex environments Instead, this paper provides an alternative attitude to approachable research methods that open up building technology innovation opportunities to untraditional innovators. When the user has control of the design/innovation process, it opens up a new realm of possibilities rich with lived experience. Understanding the movement of heat, or thermal transmittance , through materials and assemblies is critical in controlling energy consumption in buildings. Despite recent advances in computational technologies, physical testing is still the industry standard to determine a material's thermal properties. However, this process can require expensive equipment, a controlled environment for testing and deep technical knowledge to create a degree of accuracy that is not necessary for all phases of the innovation process and can even act as a barrier to the early stages of design. In this paper we propose the Approachable Hot Box (AHB), a low-cost, low-tech and accessible testing apparatus that can quickly and easily evaluate the impact of simple building materials and assemblies on ambient environmental conditions. By reducing unnecessary complexity for early-level design decisions, the AHB is consistent and reliable at an affordable price. Not only does the AHB rapidly and affordably test small material samples, it helps to articulate potentially complex concepts in a tangible and understandable way, which helps to lower the barrier to innovation. By creating a non-disciplinary, iterative design approach that uses conventional materials and accessible concepts, this work demonstrates that rapid experimentation can be used as a tool to study building assemblies or systems by non-traditional innovators. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building policies must be rooted in a good understanding of the targeted population. • Building performance standards may benefit from creating databases of weather files. • A thermal performance analysis should consider multiple key performance indicators. • Building performance policies should address the thermal resilience of buildings. This paper presents the main challenges faced and lessons learned when developing a new method to assess the thermal performance of residential buildings for the recently updated Brazilian building performance standard, NBR 15575–1:2021. It touches on subjects related to the representation of occupant behavior, climate, and thermal acceptability in building performance simulation, as well as the use of reference models, key performance indicators, and performance levels in policies. NBR 15575 provides a thorough procedure to analyze thermal performance, allowing buildings to be assessed while in passive operation mode and also accounting for energy needs when active operation is necessary. Multiple key performance indicators are introduced to provide a comprehensive evaluation. The main difficulty found was dealing with diversity. Given the vast scale of Brazil's territory, a high variation in culture, climate and construction techniques is expected, thus requiring a compromise between detail and scalability. There are still many opportunities for improvement, especially regarding the representation of occupants, and adjustments of the characteristics of reference models to promote high thermal performance, considering the diverse climates, regional practices, and economic needs. This paper might help researchers and other stakeholders to develop and improve other local standards and protocols, especially for warm climates. [ABSTRACT FROM AUTHOR]

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

• A data-informed building energy management (DiBEM) framework was proposed. • It was demonstrated in an office/laboratory building with two-years' experimental data. • The impacts of energy efficient interventions were quantified through data-driven models. • The energy use intensity was reduced from 54.1 kWh/m2 to 42.8 kWh/m2. To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. [ABSTRACT FROM AUTHOR]

• The paper explores the construction method for compact heterogeneous ensemble models. • The paper adopts the exhaustive search method for optimal model identification. • The paper proves the necessity for optimal model identification. • The paper analyzes the benefits and limitations of the exhaustive search method. Heterogeneous ensemble learning has received increasing attention in building energy prediction research because it provides more stable and accurate predictions than the conventional single model-based methods. Although existing studies have proved the feasibility of adopting heterogeneous ensemble learning in building energy prediction, they have not fully explored the method of constructing an optimal heterogeneous ensemble learning model based on limited base model resources. This paper attempts to identify the optimal heterogeneous ensemble learning model for building energy prediction by using the exhaustive search method. Six machine learning methods are used as the alternative base model for constructing heterogeneous ensemble learning models. All 57 heterogeneous ensemble learning models that can be built from the six base models are compared and the optimal model is identified. The hourly building energy prediction experiment is performed on an institutional building located at the University of Florida to verify the proposed exhaustive search method. Three modules representing different semesters of the test building are developed and tested to examine the predictive performance of the proposed heterogeneous ensemble learning models. The research findings demonstrate the feasibility of the proposed exhaustive search method in identifying the optimal heterogeneous ensemble learning model. The optimal heterogeneous ensemble learning model found in this study can reduce the MAPE by 4.9% − 6.6% compared with its most accurate base model in the model testing stage. The experimental results and research findings provide critical guidance for applying heterogeneous ensemble learning to data-driven building energy prediction. [ABSTRACT FROM AUTHOR]

• Load shifting experiments were performed in a single-family inhabited house. • Room radiators were controlled with variable set-point schedules. • Heat consumption was successfully shifted from the controlled rooms. • The total load shifting on a building level was limited. • A list of challenges for developing E-MPC in real buildings was identified. Recent simulation-based studies have indicated that load shifting using Economic Model Predictive Control (E-MPC) of radiators for residential space heating can be utilized for demand response purposes in energy systems. However, there is a lack of studies on whether this load shift potential can be realized in real, inhabited buildings. This paper reports on a field experiment aiming at realizing load shifting of space heating in a single-family house with a hydronic radiator system connected to the local district heating. Radiators in a limited number of 'active' rooms were controlled using set-point schedules mimicking the typical behavior of E-MPC. The results indicate that it was possible to load shift heating consumption in the 'active' rooms but that the load shift for the building as a whole was limited due to the hydronics of the radiator system. The paper also reports on the several practical issues encountered during the experiments – issues that in different ways are barriers to the practical realization of demand response from E-MPC of hydronic space heating systems. [ABSTRACT FROM AUTHOR]

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants' feedback, a probabilistic model of occupants' comfort, and HVAC faults detection and prediction that may affect occupants' comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants' comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage framework for monitoring the decline of pipeline insulation and diagnosing leakage fault. The task of the first stage is to estimate the parameters of the pipeline network, so as to monitor the abnormal heat conduction data of pipelines, and use the estimation results to update the parameters of the pipeline model. The task of the second stage is to obtain the location and the water loss of after a leakage fault. In the framework, the tasks of the two stages are formulated into the problem of minimizing the divergence between the real system and the simulation system. And the problem is solved by the group search optimizer (GSO) algorithm with improved boundary search strategy in this paper. In the simulation tests, the framework is verified to be able to diagnose the faults. And in comparison with other heuristic algorithms, the GSO algorithm has the best ability to accurately diagnose the fault. Finally, the framework is applied in the cooling system, which shows that the framework has good migration capability. [ABSTRACT FROM AUTHOR]

• A new hybrid model, namely CEEMDAN-SE-LSTM is proposed. • The model allows for more accurate forecasting of electrical loads. • The model is significantly better than models like EEMD-LSTM, CEEMDAN-LSTM, etc. Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN- 1 1 May 13, 2014, was chosen as the starting time because the meteorological data (e.g., temperature, rainfall, wind speed, etc.) obtained by the authors for Changsha City started from that time; and to highlight the cyclical nature of the electricity load demand, given the lack of meteorological data afterApril 2018, May 13, 2017, was chosen as the cut-off date in this paper. However, we believe that the starting and ending time does not affect the idea and method of model construction in this paper. SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models. [ABSTRACT FROM AUTHOR]

• Design criteria for active solar technologies integration in the historic built environment to favor energy transition, cleaner energy production, and climate mitigation. • Comparison of international guidelines' recommendations, design and assessment criteria. • Common glossary for the integration of active solar technologies in cultural heritage. • Definition of clear and updated design criteria valid for traditional and innovative active solar systems. • Support to solar innovation for the development of ad hoc products that considered correct design criteria. The paper focuses on architectural integration of active solar technologies (photovoltaic and solar thermal systems) in the historic built environment. Interpreted and used as a building material in the envelope, they are able to positively support energy transition and to contribute to climate change mitigation without compromising the original typological-functional characteristics. Their integration is fostered by innovative architectural design, aesthetical appeal, flexibility, multi-functionality, cost reduction, and technological development. Moreover, they can enhance economic value, functionality, and human comfort of buildings. Despite these advantages, there is a social diffidence on their acceptability due to policy, design, technical, economic and information barriers. To overcome these issues, the paper aims at identifying shared design criteria for their integration in the historic built environment, through a detailed assessment of 44 international guidelines. The study develops a taxonomy of international recommendations on energy efficiency, microgeneration, and solar integration, missing in literature so far, by analysing and comparing recurring criteria and recommendations. Also, a glossary of the most important terms according to the aesthetic, technical, and energy integration levels is created. Thus, the research defines an innovative framework of reliable design criteria, and a shared vocabulary to promote active solar technologies integration in the historic built environment. [ABSTRACT FROM AUTHOR]

Changes in the global energy landscape have increased the importance of research on energy management methods in the power grid. In particular, Demand Side Management (DSM) at consumers is attracting worldwide attention. Distributed generation in intelligent consumers, such as Smart Houses (SH), contributes to the introduction of new distributed generation from the demand side by determining optimal scheduling. However, Fuel-cells (FC) and Battery Energy Storage Systems (BESS) are expensive, and their installation is a significant burden for the demand side. Therefore, this paper proposes a Smart Apartment Building (SAB) model in which multiple distributed power sources are shared by multiple consumers to reduce operation costs and carbon emissions through the implementation of highly efficient operation methods. An important aspect of such a study is to understand the characteristics of the demand side and to propose an operating method that takes into account the preferences of the demand side. In this paper, to provide a variety of options, a Pareto front is generated through multi-objective optimization to reduce the total cost and carbon dioxide emissions of the model. The optimal component capacity is also considered at the same time. A Model Predictive Control (MPC)-based optimization algorithm is then developed to achieve highly efficient operation, thus contributing to the reduction of the two objectives. As a result, in a compromise between the two conflicting objectives, the MPC algorithm successfully reduces operation costs by 44.4% and carbon dioxide emissions by 54.7% compared to the original case. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of cities across the globe and global warming, Urban Heat Island (UHI) and building cooling energy consumption have reached a point of perilous concern. The use of shading systems on building fenestrations has long been known as an effective strategy to minimise façade heat gain as well as glare, resulting in lower cooling energy consumption and indoor comfort. Expending the potentials of shading systems beyond conventional use, the present paper focuses on the effects of shading systems on opaque facades that is currently unaccounted for in simplified Overall Thermal Transfer Value (OTTV) calculations in standards adopted by many countries. A field study was first conducted to compare the effects of various shading configurations on an opaque façade. Subsequently, a simulation study was then conducted to quantify the effects of shading systems on opaque facades with varying thermal transmittance value (U-Value). The results of this study revealed numerous benefits for the deployment of shading systems on opaque building facades, which includes, reduction to building conductive heat gain, building cooling loads, microclimate temperature near the façade, as well as potential reduction to carbon emissions over the building lifecycle. Moreover, the paper also provides considerations for the selection shading configurations, as well as the considerations that would be required for the formulation of a more accurate simplified OTTV equation that would take into account the effects of shading systems on opaque facades. [ABSTRACT FROM AUTHOR]

• An optimization strategy model of district energy system under the direct reduction/increase goal of power load is presented. • The grid can directly control the energy consumption strategy in the form of instructions. • The proposed goal pays a certain price, but a large additional flexible utilization coefficient is obtained. • The energy compensation coefficient of the multi energy coupling system is mostly negative. With the wide application of regional centralized cooling (heating) system in the world and the growing maturity of energy supply technology, the focus of system optimization gradually transforms from technical optimization to strategic optimization to attain the common goals of economy and energy saving. Aiming at the operation strategy model of power grid interactive district energy system based on time-of-use (TOU) price proposed in previous research, this paper proposes new objective equations and constraints to explore the strategic deployment of district energy system when accepting the power grid requirements to reduce or increase the power load directly to improve the economy and energy efficiency of centralized cooling (heating) system. The modified optimization strategy model is used to respond to sudden demand response or power accidents. The strategy model can be directly controlled by the power grid, and the TOU price is no longer the main factor affecting the formulation of the strategy. In the face of sudden demand response or power accidents, the operation strategy optimization model proposed in this paper can guarantee the energy supply demand of users and optimize the economic benefits and energy efficiency of the systems. The operation strategies under various optimization objectives can be obtained through the simple input of energy loss of the pipeline and user cooling or heating load. The proposed model is suitable for the load allocation of energy system with single energy supply form and for the strategy optimization of multi energy coupling system. The accuracy and validity of the proposed model under various optimization objectives are verified based on experiment and simulative research. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper summaries the present regulations and policies concerning M−TES (Mobilized thermal energy storage) in China. • The policies are analyzed based on the aspects including facility manufacture and operation, road transportation, and financial guidance. • The results can provide references for the government or policy makers to promote the development and safe operation of M−TES in China. Mobilized thermal energy storage (M−TES) is a promising technology to transport heat without the limitation of pipelines, therefore suitable for collecting distributed renewable or recovered resources. In particular, the M−TES can be flexibly used for the emergency heating in the COVID-19 era. Though the M−TES has been commercializing in China, there is not any specific regulation or standard for M−TES systems. Therefore, this paper summarizes and discusses the existing regulations and policies concerning M−TES in the aspects of facility manufacture and operation, road transportation, and financial support and guidance. Furthermore, the suggestions were presented including necessary consensus on the development of M−TES among different departments, consideration of local conditions when drafting or revising regulations and policies, sufficient investment, or subsidy on the R&D of M−TES, and qualification recognition of M−TES companies and staffs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Systematic review of hybrid system controls for mixed-mode buildings. • Meta-analysis of controls, case studies, platforms and building performance. • Optimal control most often use model predictive control or reinforcement learning. • Studies across 15 climate zones showed a medium energy saving of 28%. • 80% of case studies were simulation-based signaling need for actual implementation. Mixed-mode buildings utilise a combination of natural ventilation from operable building envelopes and mechanical systems to realise climate-friendly ventilation and cooling. The control of such hybrid systems is a promising solution to improve energy efficiency for buildings and comfort for occupants. Meanwhile, it is also a challenging task due to the complexity of the systems and coupled indoor and outdoor air flows and disturbances. This paper presents a systematic review of mixed-mode building controls. From the topical aspects of case studies, the paper analyses locations, climates, building and space types, and sensors. From a control point of view, the paper presents control algorithms based on a well-structured taxonomy, where various algorithms are classified into four categories (On-Off and PID control, rule-based control, optimal control including model predictive control and reinforcement learning, and computational intelligence including fuzzy logic and data-driven control). From an evaluation point of view, the paper illustrates evaluation platforms (simulation and real-world implementation) and results. Modelling and control tools for (co-) simulation are summarised. The evaluation results are compared based on metrics of natural ventilation and night cooling potentials, thermal comfort, indoor air quality, and energy savings. Finally, findings from the review and future research concerns are discussed. [ABSTRACT FROM AUTHOR]

• Experiment in three homes using mixed-methods and physical measurements. • Indoor air temperature fluctuations like those of EMPC of space heating. • Night setback-users found the heating control unacceptable. • Acceptance was motivated by economic or environmental benefits. Recent simulation-based studies have indicated that Economic Model Predictive Control (EMPC) of space heating systems can exploit the thermal mass in residential buildings for demand response (DR) purposes in district heating systems. However, there is a lack of studies on residents' perception and acceptance of the fluctuating indoor air temperature behaviour inherent in EMPC of space heating. This paper reports on a case study featuring the residents of three one-story houses located in Denmark. The houses were equipped with technology enabling remote actuation of radiator thermostats and collection of various indoor environmental data. Four different temperature boost interventions mimicking the typical behaviour of EMPC of radiators were executed while a mixed-methods triangulation design, employing questionnaires and semi-structured interviews, was used to collect subjective data. Data showed that residents accepted the behaviour but only after the benefits of the EMPC were explained. The acceptance was motivated by either the prospect of saving money, environmental benefits, or a combination of both. The results thereby indicate that explaining the benefits of EMPC is important if residents are to abandon their current preferences and practices in favour of an EMPC concept. The explanation should consider the diversity in current preferences, expectations, and level of technological pre-knowledge. In addition, the paper also reports on a range of findings related to technical aspects of realising EMPC in practice. [ABSTRACT FROM AUTHOR]

Owing to the challenges caused by climate change, the spread of green buildings is increasing. Moreover, it is becoming necessary to secure sustainability through the management of new and renewable energy operations. Accordingly, this paper describes the proposal and empirical demonstration of an energy operation optimization method for renewable energy-integrated buildings utilizing photovoltaics (PVs) and energy storage systems (ESSs). The proposed optimization method begins with the calculation of a weather index (WI) based on environmental and climatic data, with particular emphasis on its relevance to PV production. Additionally, we develop the weight-prox algorithm (W-PA) for controlling and optimizing daily building energy operations. The goal of this optimization method is to enhance the performance of three sustainability indicators of green buildings. Simulations are then conducted in a digital transformation environment using real green buildings as targets. The results demonstrate the improved performance of sustainability indicators, including renewable energy efficiency, economic resilience, and eco-friendliness. The output of this study can serve as a set of guidelines that considers the convenience of implementation for building operators. This study is also significant by raising awareness of the need for climate change response and contributing to the use of renewable energy for the sustainability of green buildings. [ABSTRACT FROM AUTHOR]

• Secondary students' thermal comfort was explored in the countryside throughout one year. • Comfort indicators and behavioral adaptation were determined in different seasons. • The applicability of the adaptive comfort model was demonstrated in different seasons. People experience four seasons every year, and their thermal comfort usually changes with the season. However, a little is known about the dynamic characteristics of secondary students' thermal comfort in the countryside under different seasons. This study aims to investigate thermal comfort of students in the countryside under various seasons and reveal the underlying mechanism. One year long-term field study was conducted in a countryside secondary school in Hengyang City, located in the hot summer and cold winter zone of China. A paper questionnaire was used to collect subjective thermal comfort. The surrounding physical environment was also measured. A total of 450 subjects voluntarily participated and returned 2349 valid datasets. The results indicated students had the lowest acceptance rate with temperature (71.9 %), humidity (74.9 %), and velocity (70 %) in summer season. Neutral temperature was 25.7 °C in summer, 19.2 °C in transition, and 14.9 °C in the winter. An inverted U relationship was found between perceived air quality and air temperature. Cold extremities (53.0 %), shivering (37.2 %), and stuffy nose (60.4 %) were prevalent in winter. Adaptive comfort model was only effective in the summer in naturally ventilated secondary school buildings. Estimated learning performance was the highest in transition and lowest in summer. Behavioral adaptation was determined by the relationship between air velocity clothing insulation and operative temperature. The findings of this study provide fundamental knowledge of thermal environment, subjective comfort, and health status in naturally ventilated educational buildings in countryside area. Engineers and designers can use professional comfort indicators to guide their future construction or renovation. [ABSTRACT FROM AUTHOR]

One of the major challenges with increasing penetration of renewable energy sources (e.g., solar and wind) is to maintain grid stability. Grid-interactive efficient buildings are regarded as a promising approach to advance the role buildings can play in energy system operations and planning. This paper evaluates the demand flexibility in a residential building located in Karlsruhe, Germany, incorporating multiple distributed energy resources, including a photovoltaic and battery system, an electric vehicle, an electric water heater and a heat pump. A multi-criteria optimization problem is formulated with model predictive control (MPC) for five cases of demand flexibility. The simulation results of five winter days show that the peak power during the peak periods can be reduced by 92%, 69%, 100%, and 100% for real-time pricing, demand limiting, load shedding, and load shifting, respectively. For the power tracking scenario, the MPC tracked the reference power profile successfully for 70% of the simulation time. Achieving demand flexibility does not necessarily cause an increase in energy costs. • Distributed energy resources include thermostatic loads, PV, battery, and electric vehicle. • Real-time pricing, load shedding, shifting, and power tracking are considered. • An approach to maximize load sheaving and shifting is proposed. • Providing demand flexibility does not necessarily increase energy cost. [ABSTRACT FROM AUTHOR]

• Experimental campaign performed to test the operation of a return-to-supply bidirectional substation. • Hardware-in-the-loop technique coupling in real-time realistic thermal load and production profiles. • Detailed tests for representative days, three case studies and extended assessment for the entire year. • Daily district heating contribution ranging between 21% and 100%. • Yearly local production contribution reaches 100% of the user's load, considering the net-metering. The regulatory European and national policies outline the role of efficient district heating in achieving mid and long-term decarbonization targets. Prosumers could be equipped with bidirectional exchange substations to increase the local production contribution. The present paper evaluates the contribution of a bidirectional substation in maximizing the use of thermal energy produced locally by renewable sources or waste heat in a densely populated area, considering the limitation due to the non-contemporaneity of production and consumption and possibilities to overcome them. The substation prototype and the control system were tested in operational conditions, assuming the district heating network operating at two temperature levels (80 °C/50 °C and 60 °C/30 °C for supply and return) and applying the hardware-in-the-loop technique for coupling the substation in real-time with data-driven thermal loads (multi-family residential building) and production (solar panels and waste heat) profiles. The various energy flows in the substation are presented for representative days, resulting in a district heating contribution to the user's load ranging between 99.8 % (winter day) and 21.8 % (summer day). The net-metering mechanism was considered, and the assessment extended to the entire year. The local production contribution to the user's load ranges between 24 % and 100 %, depending on the system configuration considered. [ABSTRACT FROM AUTHOR]

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • The cost-of-living crisis affected energy use in winter 2022/23 in British households. • Domestic electricity consumption dropped 8.4%, gas consumption dropped 10.8%. • Reducing heating was most effective; underheating and fuel poverty are key concerns. • Energy reductions saved consumers around £29/month on their bills. • Winter 2022/23 energy bills were still £34/month higher on average than before. In October 2022 British households entered a heating season amidst exceptionally high energy prices – squeezing household incomes and increasing fuel poverty. This study analyses electricity and gas consumption in 5594 households from October 2022 to March 2023 using XGBoost counterfactual models trained on historic data. With survey data collected in early 2023 we investigate how consumption reduction correlated with energy-saving actions, household and dwelling characteristics, and indicators of underheating and fuel poverty. Our analysis showed that electricity consumption was 8.4% lower and gas consumption 10.8% lower than the previous winter (accounting for weather), saving consumers around £29/month. Despite this and a government subsidy, energy bills were still around £34/month higher than the previous winter (£158/month (median); £500/month (95th percentile)); price elasticity was −0.10 for electricity and −0.07 for gas consumption. Greatest consumption reduction correlated with largest reported changes to heating practices, in particular heating for fewer hours and turning thermostats down lower. We find evidence of greater fuel poverty and underheating among the greatest energy reducers. This paper presents novel methods for analysing energy saving using smart meter data for changes without a control group, plus novel findings related to short-term price elasticity and the energy-saving impacts of behaviour change. [ABSTRACT FROM AUTHOR]

• Traditional DALT-type tests allow the classification of ductworks but do not allow the direct estimation of actual leakages. • The Ashrae 215 Standard allows measurement of leakage under operating conditions but not classification. • The proposed method allows both classification and estimation of leakage under operating conditions. • The method is based on a more accurate model which requires a simplified test with shut-off dampers and the appropriate information from the dampers' manufacturer. • The method has been validated through experimental tests. Reducing air leakages in HVAC systems is potentially one of the actions with the best potential for energy savings in the residential sector. The assessing of air leakages in HVAC systems is currently addressed in two ways: i) the traditional DALT (Ducts Air Leakage Tests) to characterize and compare ductwork and ii) the ASHRAE 215:2018 standard method to measure leakage at nominal working conditions. In this paper an innovative experimental method for assessing air leakages in HVAC systems at operative conditions is proposed. The method is based on flow rate measurements at different pressures and performed with closed terminal dampers. The so called "Shut-Off Method" has been then experimentally validated in a real plant showing good agreement with the ASHRAE 215 procedure. Finally, on the basis of the Shut-Off model application, the leakage coefficient used by the standard DALT classification procedure was calculated, showing leakage performance 2,3 times worse than the Eurovent Class A requirements. [ABSTRACT FROM AUTHOR]

Ground-Coupled Heat Pumps (GCHPs) are regarded as an effective technology for building energy development and residential hot water production. The essential challenges to encompass these systems in urbanized areas are the highly drilling and initial costs of GCHPs. To overcome this challenge, researchers and industries are seeking innovative solutions by focusing on optimizing the structure of ground heat exchangers (GHXs), which act as the essential component for efficient geothermal energy extraction in GCHPs. Hence, this paper introduces a comprehensive comparative numerical investigation of an innovative U-tube GHX with a hollow-finned structure for augmenting the performance of the GCHPs. This design remarkably enhances the thermal performances, reduces the thermal resistance, therefore minimizes borehole depth, reduces drilling costs, boosts GCHP's financial viability, and bolsters geothermal energy utilization. Therefore, a series of three-dimensional analyses of transient heat extraction is executed and validated using a practical field experiment performed by the authors' research group to investigate the thermal behavior of the hollow-finned U-tube GHX at different fin configurations and compare its efficacy to typical circular U-tube GHX. More so, a parametric study and sensitivity analysis are also performed to identify the influence of critical operational and design factors on the heat extraction of the proposed hollow-finned U-tube GHX compared to the typical circular GHX. The influences of groundwater flow on the transient heat extraction were also examined. Additionally, a dimensionless shape factor is introduced to assess the implications of the U-tube geometry with hollow-finned on the thermal performance of the GHXs. Furthermore, design optimization was also conducted by examining different branched-shaped hollow-fin's U-tube GHX configurations. The findings indicate that the mean heat transfer rate of hollow-finned U-tube GHX can be increased by 44.58 % compared to the conventional circular U-tube GHX. Correspondingly, the total thermal resistance of hollow-finned U-tubes GHX was reduced by 47.71 %. It also identified that the Y-shaped hollow-finned U-tube GHX was the optimal design among the investigated configurations of branched-shaped hollow-finned U-tube GHXs, demonstrating an increase of 52.64 % compared to the conventional circular U-tube GHX. Moreover, it is revealed that a lower shape factor enhances the heat transfer rate significantly. Furthermore, the optimal mass flow rate and desired number of hollow-finned were estimated as 0.80 kg/s and six fins, respectively. Conclusively, this investigation concludes that the hollow-finned U-tube GHX can greatly enhance the heat extraction rates and reduce borehole thermal resistance, which can increase the efficiency of GCHP systems. [ABSTRACT FROM AUTHOR]

• The average heat exchange per meter of 2000 m DBHE is 141.5 W/m at coldest moment. • The recommended insulation length of 2000 m DBHE is 178 m. • One 2000 m DBHE possesses a TER of 13.0 m for one heating period. • The load carrying capacity of 0.04℃·m−1 is 1.33 times better than the 0.03℃·m−1. The deep borehole heat exchanger (DBHE) is a vital part in medium-depth geothermal heat pump system (MD-GHPs). In comparison to other forms of heat exchangers, the DBHE has the following outstanding advantages: the small footprints, the little damage to rock as well as the high efficiency. However, numerous DBHE models were fixed variable values such as flow rate or inlet fluid temperature and there were few studies combining the user heating load variation with the temperature fluctuation on the DBHE side. In order to fill this gap, a DBHE heat transfer model with dynamic building heating load constraint is developed. In this heat transfer model, the load boundary condition is the hourly flow rate and the difference in inlet and outlet water temperature, and the outlet water temperature is obtained through iteration. In addition, this model is validated by a building space heating experiment data in Harbin and guaranteed its correctness. The average heat exchange per meter at the coldest heating moment also ensures the viability of MD-GHPs, which is 141.5 W/m. A series of typical coordinates are used to analyze the effect of DBHE on the surrounding rock, and the concept of DBHE recommended insulation length are proposed. One single DBHE with 2000 m burial depth possesses a temperature effect radius of 13.0 m for one heating period. Besides, some critical conclusions are as follows: For the structural parameter, the recommended insulation length is 178 m. For the operating parameter, there is a minimum value for the total power consumption and 18 m3·h−1 is the recommended optimal flow rate in this situation. For the geological parameter, raising the geothermal gradient can dramatically improve inlet fluid temperature (IFT) and outlet fluid temperature (OFT), and enhance the ability of DBHE to carry the building heating load. When maintaining the same IFT at the coldest moment, the geothermal gradient of 0.04℃·m−1 is 1.33 times more capable of carrying loads than the 0.03℃·m−1. This paper can provide basis for a series of DBHE thermal performance in practical space heating engineering applications of MD-GHPs. [ABSTRACT FROM AUTHOR]

Nowadays, energy management assumes a pivotal role in ensuring the stable operation of microgrids, facilitating enhanced monitoring, analysis, and optimization of energy utilization for end-users. The implementation of an energy management system (EMS) offers invaluable assistance to microgrid users, enabling them to cut electricity costs, ensure reliability, and provide an elevated level of comfort. However, as the scope of microgrid management proliferates, balancing between the comfort of community microgrid users with more devices and their profitability becomes increasingly challenging. To address this issue, this paper proposes a novel community control approach for EMS, called CuEMS, that utilizes deep reinforcement learning (DRL) to manage multiple appliances and energy storage systems (ESSs). The proposed approach optimizes energy profitability for operators while taking into account user comfort. It reformulates energy scheduling as a Markov decision process and learns the optimal scheduling strategy through deep Q-networks. The presented CuEMS approach is evaluated using real world data from Finland, and the results demonstrate that it can optimize the operation of multiple appliances and improve the profitability and user comfort compared to other methods. • A deep reinforcement learning-based DQN approach for community microgrid management. • A balance between maximizing the energy profitability of the community operator and the comfort of the users. • A centralized framework for energy scheduling of multiple types of devices. [ABSTRACT FROM AUTHOR]

Efficient cooling of the trans-critical CO 2 vapor compression cycle to improve system performance is a crucial research topic. This paper proposes an innovative approach called distribution compression (DC) to achieve the same effect as subcooling in a trans-critical CO 2 thermodynamic cycle. In the DC system, the trans-critical CO 2 at the gas cooler outlet is no longer further cooled but subjected to a secondary pressure boost and releases heat in ordinary heat sink conditions as in the gas cooler. Thermodynamics calculated the change in performance of the DC system under different working conditions with different secondary boost ratios. The optimal secondary boost ratio related to evaporation and gas cooler outlet temperatures under optimal discharge pressure based on the baseline system was obtained. The results show that compared with the baseline system, the DC system can effectively improve the system performance, and the maximum refrigeration COP increase is between 8.2 % ∼ 10.76 %, and the maximum heating COP increase is between 5.37 % ∼ 6.34 %. The cooling or heating capacity increase can reach up to about 26 %. The ideal secondary boost ratio in a DC system is not highly demanding, and the increased input power for the second input power for the secondary boost will not exceed 20 % relative to the baseline system. Comparing the COP of the DC system over current systems that only use a single subcooling technology still shows advantages. The DC system provides a new idea to improve the performance of the trans-critical CO 2 vapor compression cycle. [ABSTRACT FROM AUTHOR]

• Integration of industrial buildings and shared storage is studied. • Peer-to-peer energy trading between industrial buildings are considered. • Risk assessment of industrial buildings are investigated. • Risk is managed using robust optimization and CVaR methods. Local power markets and peer-to-peer (P2P) power transaction plans between the clients have lately received attractions as a cost-effective approach to encourage flexible energy options and sharing the internal power resources. For the integration of industrial consumers (ICs) in the presence of electric vehicle parking lot, this study modeled local power markets to share the surplus power of ICs between each other. Other hands, shared storage is modeled in the proposed system to benefit consumers. In order to investigate the proposed system under uncertain conditions, a hybrid optimization method is proposed for uncertainty modeling. In this paper, the well-known uncertainty modeling tools, including the robust optimization and Conditional Value at Risk (CVaR) embedded stochastic optimization approach, are proposed to model the uncertainties. The markets price uncertainty is one of the important uncertain parameters assumed as an unknown-distribution parameter due to insufficient historical data or not following a specific distribution; thus, the risks of this parameter are modeled by robust optimization, which has no need for big historical data of uncertain parameter. The other uncertain parameter is solar irradiation. The corresponding risks of solar irradiation is modeled by CVaR modeling because of the following Beta distribution. Thus, the financial risks of different uncertain parameters can be modeled effectively using the proposed hybrid optimization framework. According to obtained results, using peer-to-peer trading reduces the operation cost by 7.94%, while using the shared storage along with peer-to-peer trading reduces the operation cost by 21.49%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Identifying recent developments in thermal-driven dehumidification technologies. • Evaluating critical characteristics including dehumidification efficiency, energy consumption and cost. • Assessing the desiccant properties for thermal driven dehumidification technologies. • Directing future research to address environmental concerns via innovative sustainable humidity control mechanisms. As we navigate an era marked by significant environmental concerns and technological advancements, the search for energy-efficient and ecological friendly solutions is more critical than ever. One area of growing significance is the control of indoor humidity levels, which not only impacts human comfort and health but also the structural integrity and air quality within buildings. The challenge is to control these levels while lessening energy consumption and environmental impact. A feasible solution lies in the use of thermal-driven dehumidifiers, a promising technology that offers a sustainable approach to humidity control. Thermal-driven dehumidifiers have emerged as promising solutions for humidity control in various applications, owing to their energy-saving and eco-friendly features. Thus, the focus of this review paper is to explore the most recent advancements and enhancements in thermal-driven dehumidification technologies. This includes systems based on solid desiccant, liquid desiccant, and thermoelectricity. The study aims to fill the existing literature gaps, providing a thorough analysis of the current state of these technologies, their performance indicators, and their potential for future development. The study also explores the suitability of these technologies for different ambient conditions and humidity loads. It addresses critical characteristics such as dehumidification efficiency, cooling capacity, regeneration heat, and energy consumption. Each technology is thoroughly examined, including its suitability for various ambient conditions and humidity load scenarios. Specific real-world examples are examined to highlight these technologies' practical importance and possible influence in tackling humidity management concerns. [ABSTRACT FROM AUTHOR]

This paper proposes a framework applicable to preliminary building design that cohesively integrates material cost estimation, embodied energy assessment, and the prediction of the thermal demand of a building. At its core is a climate-responsive machine learning model specifically developed using readily accessible early-stage design data. This model forecasts the thermal demand both in the present and in the face of likely climatic shifts. To strike a balance in energy performance goals, material cost estimation is seamlessly incorporated. Building on the gradient boosting machine, the proposed approach showed better performance than other algorithms, predicting thermal demand 25 to 37.5 times faster than conventional energy software without compromising accuracy. The key findings of the presented case studies highlight the significant influence of the Window-to-Wall ratio and floor thermal mass on thermal demand—especially in mitigating cooling demands posed by potential climate change scenarios. By enhancing interior floor types, heating demand can be reduced by nearly 49%. The study further suggests that incorporating climate change considerations into life cycle energy designs is projected to amplify thermal and cooling loads by 32% and 94%, respectively, highlighting the crucial role of early design decisions in shaping sustainable and efficient buildings. • A framework estimates cost, embodied energy, and predict thermal load in pre-design stage. • This framework integrates climate changes for energy forecasting over the life cycle. • Floor thermal mass emerges as a key predictor in temperate climates. • Varied floor materials and types and their impact on thermal demand are detailed. • Slab on the ground and WWR are found to mitigate cooling demand amid climate changes. [ABSTRACT FROM AUTHOR]

The application of phase change material (PCM) in Trombe walls (TW) can improve all-day indoor thermal comfort. However, the heat loss via the glass is relatively large, resulting in low thermal efficiency at night. This paper designs a double glass and a thermal insulation curtain used to increase the thermal efficiency of a TW with PCM at night. In addition, a mathematical model based on the implicit difference method was established to study the thermal efficiency and heat loss of the proposed wall in Beijing. The findings indicate that the thermal efficiency at night is improved by 16.23 % compared to a traditional TW, while heat loss at night drops by 7.75 %. The heat gain at night first increased and then decreased with the augment of phase change temperature. As the latent heat or thermal conductivity of PCM increases, the heat gain declines during the day and increases during the night. The most suitable thickness of PCM is 0.02 m. The farther the insulation curtain is from the inner glass, the greater the heat loss and the smaller the indoor heat gain at night. And the later the opening time of the air vents during the day, the smaller the heat gain during the day and the greater the heat loss. But the resulting increase in nighttime heat gain is not significant. This study can offer design suggestions for enhancing indoor thermal comfort and nighttime insulation performance. [ABSTRACT FROM AUTHOR]

Net-zero energy buildings produce enough renewable energy to meet their own annual energy requirements and have been proposed as an important method of reducing operational greenhouse gas emissions in the building sector. However, self-generation via building-integrated solar PV in cold and temperate climates is anti-correlated with increasingly-electrified heating demand and could increase seasonal variability in net electricity demand to the point that it poses challenges for future electricity systems. In this paper we explore scenarios of future large-scale uptake of electrically-heated, solar-PV net-zero energy residential buildings in New Zealand and quantify the seasonal variation of the resulting net electricity demand. A scenario of large-scale uptake of very high efficiency buildings leads to reductions in annual demand of −30% and seasonal variation of −50% in 2050 compared to a base case of current building standards. In comparison, a scenario with self-generation via solar PV without changes to the building standard reduces annual demand by −30% but increases seasonal variation by +40%. In a scenario where very high building standards are combined with solar PV, annual demand decreases by −65% and seasonal variation by only −4%. From a policy perspective, whether large-scale solar PV self-generation should be supported (in addition to very-high efficiency buildings) depends on an economic trade-off between the value of distributed solar generation (including any carbon emission reductions) vs the electricity system cost of seasonal variation. For New Zealand, given the low cost of renewable electricity from a variety of alternative sources, scenarios of large-scale uptake of solar-powered net-zero energy buildings are not favourable from an electricity system perspective. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposing a novel approach to predict energy consumption in mosque buildings during the operation stage. • Simulating various scenarios involving different operational variables of the mosque and the utilized systems. • Developing a Convolution Neural Network (CNN) deep learning model to predict the mosques' annual energy consumption. • Comparing the developed deep learning model against the Support Vector Regression (SVR) model. The energy consumption resulting from the construction sector is increasing rapidly. Different building types have different energy consumption schemes, especially during their operational stage. Among these types of buildings, mosques are considered one of the intermittent building types that suffer from huge energy wastage. That's why there is a need to monitor their operational performance through retrofitting or changing their operational scheme to enhance their operational performance and examine the effect of different alterations on the annual energy consumption. However, using traditional methods and simulation models to examine the impact of different alterations on the annual energy consumption of the mosque is considered difficult and time-consuming. Using machine learning and deep learning models, energy prediction models facilitated energy consumption predictions for improving building energy utilization. Therefore, this paper proposes an efficient approach for predicting mosques' energy consumption using a deep learning approach. Convolution Neural Network (CNN) deep learning model is developed to predict the annual energy consumption of mosque buildings based on various operational Scenarios. 3D Laser scanning and a detailed energy analysis model with various simulation results are used to generate the mosque's simulated energy consumption dataset. Different operational variables, such as the operational schedule, division of the mosque into zones, utilization of dimmers, presence of cooling system and the setting point, and the power consumption of the lighting system and appliances, have been utilized to estimate the annual energy consumption for the mosque to formulate the database. Using the mosque's simulated energy consumption dataset, the CNN model was trained and tested to obtain the best model configuration with regard to the defined performance metrics. Besides, the performance of the developed model has been compared with the Support Vector Regression (SVR) model. The developed model has achieved results of 4.5% and 0.98 for the MAPE and R2, respectively. The results revealed the developed CNN model's effectiveness for predicting energy consumption for mosque buildings. This model will help compare different operational strategies for mosque buildings and determine energy consumption and energy-efficient options based on different operational characteristics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flexumers have four main energy functions: consumption, generation, storage, and flexibility. • An innovative and extensible definition applicable to flexumers. • A comprehensive summary of the key technologies on which flexumers rely. • The four major multi-dimensional applications of flexumers were summarized. • There are five possible future works to activate buildings to become flexumers. Buildings have undergone a significant transformation in their energy role, from consumers to prosumers and now to flexumers—a concept that signifies the flexible nature of modern buildings in the energy domain. However, existing research lacks a comprehensive definition of the transformative features of flexumers, as well as limited knowledge about their technologies and applications. This paper aims to fill this gap by providing a comprehensive summary of the new energy roles of buildings through a literature review. By reviewing current common definitions and classification frameworks of nine popular terms related to building energy, this study proposes innovative and basic definitions applicable to flexumers that can serve as a paradigm for adaptive expansion. The flexumer combines four main energy functions: consumption, generation, storage, and flexibility. These functions are dynamically balanced and optimized throughout the building's life cycle. Furthermore, flexumers have a positive impact on the grid and the wider energy system, as they can adjust their energy status in real time to synergize and complement the energy network. The long-term goal of flexumers is to achieve positive energy using only renewable sources, while simultaneously balancing zero carbon emissions and minimizing energy costs. The key technologies that enable buildings to become flexumers are comprehensively summarized, with a primary focus on electrical system energy efficiency, on-site energy generation, energy storage systems, and interactions with energy networks. This study details specific operational strategies feasible for each technology and compares them in terms of difficulty, cost, and interrelationships. Additionally, the study summarizes four major energy applications of flexumers: positive energy districts, resilient communities/cities, virtual power plants, and synergy with transportation. Based on the review results, five feasible directions for future work are proposed to activate buildings as flexumers. This study provides the first systematic summary of the roles and technologies of buildings in the energy field and offers a valuable reference for stakeholders in addressing the challenges they face in building energy and resolving contradictions as they strive towards carbon neutrality. [ABSTRACT FROM AUTHOR]

Building archetype identification is crucial for Urban Building Energy Modeling (UBEM), but is still considered one of the biggest challenges in this field. New methods of data acquisition, along with data mining techniques such as clustering, have recently received attention for the possibility of significantly increasing identification reliability and archetype accuracy. This paper aims to establish a new and simple clustering methodology for developing building archetypes for hybrid UBEM, using open data sets and multiple diverse variables, that is still reliable and possible to validate without the use of metered energy use or real building data. The methodology uses k -means clustering for 10 building parameters simultaneously, including socio-economic parameters obtained using spatial interpolation of statistical values. Building archetypes are successfully developed for the residential building stocks of two case study areas in Sweden. The results also show that the error metric values for multiple iterations diverge after a certain number of clusters, even when using the same clustering methodology on the same data set. This discovered effect, along with the combined use of one well-known and one novel error metric, constitutes a framework well adapted to accurately determining the optimal number of building archetypes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Numerical heat transfer models of PV, PV/PCM and PV/PCM/PHP are established and experimentally validated during diurnal cycle. • Structural parameters of pulsating heat pipes are evaluated for heat transfer process between evaporation and condensation section. • The improved numerical method for PV combined with PCM and PHP cooling modules is established for diurnal cycle. • PV, PV/PCM and PV/PCM/PHP temperature distributions and power generation performance during diurnal cycle are analyzed for different climate zones. • When applying PV/PCM/PHP in a cold area (Shenyang), its temperature control performance is the best. The surface temperature of photovoltaic (PV) modules is a key factor affecting the efficiency of photoelectric conversion. Passive cooling technology plays an important role in PV cooling, and coupling improvements to different heat dissipation methods can improve photovoltaic heat transfer efficiency and service life. This paper proposes an improved method for numerical calculations of long-duration heat transfer processes, targeting a combination of various cooling methods. The numerical heat transfer model is established for the PV panel coupled with the phase change material (PCM) and pulsating heat pipe (PHP) cooling modules. The temperature distribution of the PV panel is experimentally verified in the long-term heat exchange process. The simulation study is performed to investigate the influence of geometric structure and physical property variations of PCM and PHP on the heat transfer performance of PV panels. Evaluation of coupled cooling modules is conducted for temperature control of PV under different meteorological conditions. In the experimental validation, the PCM of the PCM cooling module is made by choosing paraffin and tetradecane-doped graphite, and the PHP cooling module is designed according to the parameters of the optimal case from numerical results. In the climate applicability analysis of the PV/PCM/PHP coupling module, the established heat transfer model is used for the study of temperature control effects during day-night cycles and the calculation of photoelectric conversion efficiency, and the applicability to different climatic regions is evaluated. [ABSTRACT FROM AUTHOR]

• A novel support framework for thermal performance analysis with best-fit indicators. • Developed based on existing industry practices and state-of-the-art literature. • Climate change-sensitive analysis on a timber dwelling shows significant overheating. • Indoor overheating degree values increased by 101% towards the end of the century. • The framework will help decision-makers in effective thermal performance evaluation. This paper proposes a novel multi-criteria decision support framework for climate change-sensitive evaluation of building thermal comfort performance in European buildings. The proposed framework considers various comfort categories based on building use and condition, comfort models based on building operation, sensitivity to climate change, key performance indicators, and their thresholds. The proposed framework is then implemented using a passive house-certified high-performance timber dwelling near Brussels as a case study. The thermal comfort in the free-running reference timber dwelling is assessed using a whole-building energy performance simulation model with a static threshold of 26 °C for the bedrooms and an adaptive threshold for the other occupied zones. The analysis found an increase in indoor overheating degree by 1.7 °C and ambient warmness degree by 6.5 °C during heat waves from the current scenario to the end of the century scenario. However, the reference timber dwelling effectively suppressed the impacts of climate change with varying degrees of success towards the end of the century. The proposed framework is intended to support decision-makers in effectively evaluating performance during the early stages of building design. The study highlights the need for further research on building performance assessment techniques and guidelines in a changing climate. [ABSTRACT FROM AUTHOR]