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Previous studies have shown that coincident design day (CDD) can make the calculated design cooling load of an air-conditioning system more rational and accurate. The conversion process of room heat gain to cooling load in radiant cooling systems is different from that in convection cooling systems. The radiant time series method (RTSM) of generating the coincident design day is inapplicable to radiant cooling systems. In this paper, the radiant and convection time series method (RCTSM) is proposed to generate the coincident design day for radiant cooling systems. The relative deviation of design cooling load, computation time for generating coincident design days, and the correct rate are introduced to evaluate the applicability of RCTSM for generating coincident design days. Fifteen room/building characteristic parameters are taken into account to construct 10,000 simulated sample rooms for each city. Compared to the theoretical design cooling load, the relative deviations of design cooling load determined by RCTSM are generally within 7.23 %. RCTSM is suitable to calculate design cooling load and generate coincident design day for radiant cooling systems. The computation time of RCSTM for generating coincident design day is less than one of sixty-third of that of heat balance method. RCTSM can significantly enhance the efficiency in generating coincident design day. The correct rates of the coincident design days generated using RCTSM is higher than those using heat balance method. These results indicate that RCTSM is more suitable to generate the coincident design days for radiant cooling systems than heat balance method. [ABSTRACT FROM AUTHOR]

The paper aimed to investigate the eco-lightweight concrete external walls suitable for the hot-summer and cold-winter (HSCW) zone, utilizing locally available renewable materials. The study was devoted to two types of concrete: one without wheat straw (C-W-WS) and another containing wheat straw (C-WS). The effect of wheat straw addition on physical and mechanical properties, thermal and hygroscopic properties, as well as the non-air-conditioned thermal environment and energy consumption of buildings, had been discussed. The results indicate that the maximum reduction in the thermal conductivity of C-WS is 13.70 % by altering the straw content and particle size. Moreover, the results of using DesignBuilder software and its application on external walls made of these materials show that, in the case of C-WS, the daily mean indoor temperature is improved compared to the case of C-W-WS. In addition, based on Adaptive Predictive Mean Voting (APMV), buildings with C-WS external walls achieved a mean improvement of 4.37 % in meeting thermal environment standards compared to buildings with C-W-WS external walls during summer design week. Moisture absorption by external walls increased energy consumption, and the annual air conditioning energy-saving of the Chongqing rural residence with C-WS external walls was 3.12 %. [ABSTRACT FROM AUTHOR]

This paper proposes a transfer learning (TL)-based control-oriented model development framework. In particular, this study examines the transferability from virtual (source) to existing (target) buildings to overcome the data imbalance issue of the data-driven approach. The target system is a cooling system comprising two supply air fans and four condensing units. First, synthetic data rich enough to provide fundamental knowledge about the target system were generated using the EnergyPlus model. A data-driven model was subsequently developed to learn the underlying dynamics of the system. By adopting TL using an imbalanced dataset measured from the target system, the knowledge that the model learned from the virtual data was transferred to the target system of the existing building. The results showed that the transfer learning model could accurately describe the dynamic behavior of the target system and predict the supply air temperature with marginal errors (CVRMSE: 5.4%, MAE: 0.96 ℃). In other words, the TL from virtual to existing buildings can overcome the data imbalance issue for developing a reliable data-driven model. [ABSTRACT FROM AUTHOR]

Prior studies tested the environmental Kuznets curve hypothesis via the income channel, but few studies have used the housing market to examine this relationship; these papers are constrained due to the fact that CO 2 emission was used as a measure of environmental deterioration, which only accounts for air pollution. Therefore, this study aims to bridge this gap by employing the housing market to test for the validity of the environmental Kuznets curve using ecological footprint in the case of the USA, which serves as a more comprehensive measure of ecological equality. The present work is the first study to explore the impact of the housing market on ecological footprint in the context of the USA. The study uses the novel bootstraps ARDL approach to assess the effects of the housing market, renewable energy, economic growth, and economic complexity on the ecological footprint in the case of the USA from 1980 to 2018. The outcomes revealed that the impacts of renewable energy and economic complexity on ecological footprint are positive and statistically significant. In addition, the environmental Kuznets curve theory has also been validated in the case of the USA. The empirical results also indicate that the housing market adversely influences ecological quality. Based on the study findings, this study offers several policy implications for the USA policymakers to promote ecological sustainability by formulating comprehensive economic and green energy policies. Furthermore, the study suggests that USA policymakers should promote a high level of complexity through knowledge and skills to achieve a sustainable ecosystem. [ABSTRACT FROM AUTHOR]

Cost of energy consumption is one of the biggest operational cost for airports, and it is increasing from time to time as airports expand to support growing number of passengers. Various factors affect the energy consumption including efficiency of airport Heating Ventilation and Air conditioning (HVAC) systems, which in turn depends on the efficiency of individual subsystems. In this paper, we present an optimal scheduling method for the central plant system at Dallas Fort Worth airport, involving chillers, pumps, and a thermal energy storage (TES) system. A model predictive control (MPC) problem is formulated to minimize both energy and demand charge costs while satisfying the cooling needs of the airport. The proposed Mixed-Integer Nonlinear Programming (MINLP) formulation includes performance curve based models for chillers and pumps and a simplified state of charge model for TES. The formulation also includes predictions of cooling load and chilled water return temperature. Simulation results for a month in summer show savings around 10% compared to the baseline. Initial recommendations based on insights from simulation results to the manual operation procedures resulted in significant savings. Field test results show a 7% chiller efficiency improvement. [ABSTRACT FROM AUTHOR]

Increasing the share of renewables, especially utilizing solar power in our energy mix is a highly prioritized aim of the EU. Budapest, the largest city and the capital of Hungary, contains almost 1 million flats in more than 190 thousand buildings, organized in various characteristic urban fabric types. Although on-site renewable energy production is severely limited in dense urban environments, the roofs offer a considerable potential to generate energy on-site. The present paper introduces a method to estimate the PV potential of rooftops on a large scale applying a GIS approach. Using LiDAR data, a Digital Surface Model (DSM) was created. The DSM was utilized to evaluate roof slope, orientation, and global irradiation. Solar potentials, available roof area, photovoltaic (PV) system capacities and estimated annual electricity production were derived from the GIS model. The method was used to develop an open access website called the Budapest Solar Map, which provides decision-makers with solar PV data. However, the nominal PV potential is limited by layers of heritage protection regulations that safeguard the sight of our case study area, Budapest, which is a World Heritage Site. Visibility is also surveyed with GIS and geometrical modeling from designated high points and from the street level, respectively. [ABSTRACT FROM AUTHOR]

• One-year microclimate was measured on the rooftop of a 26-storey office building. • Multiple methods were used to validate energy model of high-rise office buildings. • Fan efficiency and cooling setpoint were found to mostly affect building energy use. • Envelope, orientation and window blind had modest impact on building energy use. • Total building energy use can vary by 60% by using various energy saving measures. Office buildings are a major contributor to energy consumption in high-density urban contexts where building high-rises is a norm. It is important to understand the parameters affecting high-rise office buildings' energy consumption. However, previous research generally used isolated buildings as target buildings, regardless of the impact brought by the surrounding buildings. Also, there lacked a comprehensive calibration method to validate the energy baseline model. This paper aims to quantitatively analyse the energy use in high-rise office buildings by tackling these two issues and examining the impacts of various energy-saving measures (ESMs). A real-life 26-story office building in Hong Kong was selected as a base case. Multiple methods were used to calibrate the building energy model, including onsite microclimate measurement, metered data, architectural drawings and official documents, and onsite surveys. Parametric sensitivity analyses were then conducted to quantify the importance of ten selected parameters from five types of ESMs. Results indicate that fan efficiency and cooling setpoint temperature had the most significant energy-saving potential. The base case of the ESMs in use was considered as the base scenario. Two new scenarios were proposed to examine the range of total energy consumption by adopting the most and least energy-efficient ESMs, which were referred to as the "optimistic scenario" and the "pessimistic scenario". The maximum reduction/increase in energy use is expected to be approximately 13.2%/39.3% for the optimistic/pessimistic scenario compared to the base scenario. The findings on the sensitivities and scenarios contribute to a more comprehensive understanding of the critical parameters that influence energy use in high-rise office buildings in subtropical climate. The findings also provide scenario-based design solutions for delivering low-energy office buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Urban microclimate tools are effective to test the impacts of climate adaptation strategies. • Out of 134 papers, 18 microclimate tools are deeply reviewed. • Their features and capabilities are compared and contrasted for a supporting tool selection. • Reviewed microclimate simulation tools show limitations and adopt strong assumptions. • Assumptions and limitations must be accounted for a reliable use of these tools. The impact of human activities on climate change has become increasingly evident, with cities being particularly vulnerable to its effects. Anthropogenic emissions, such as heat and greenhouse gases, are projected to intensify climate-induced phenomena, which can lead to negative health outcomes. To understand how human health would be affected by such climate-exacerbated phenomena, computational models that consider the local microclimate are essential to better regulate cities to respond to these phenomena. Many simulation tools have been created and enhanced over the years. Therefore, this study systematically reviews the currently available urban microclimate simulation tools and compares their features and capabilities. The review suggests that these models can effectively assist in investigating urban health and testing adaptation strategies, but it is important to acknowledge their limitations due to assumptions made. Nonetheless, with proper interpretation and utilization, these models can provide valuable insights and contribute to informed decision-making processes. [ABSTRACT FROM AUTHOR]

Due to the long time since their construction, some old buildings have experienced a decline in performance and do not meet contemporary requirements. Retrofitting methods, as opposed to demolition and reconstruction, are considered a more cost-effective and energy-efficient approach, particularly applicable to historical buildings with conservation significance. Exterior windows account for a significant amount of heat loss within the building envelope, but it is a challenge to improve its performance while retaining the historical appearance of the building when retrofitting. In this paper, a new design of the window-wall interface structure was proposed, which facilitated the incorporation of additional thermal insulation materials and mitigated the thermal bridge effect at the joint between the window frame and the wall. Comparative analysis with existing window-wall interface structures revealed the superior thermal performance of the proposed structure, with less heat transfer as well as smaller surfaces at risk of condensation. The structure was first implemented in a renovation project in Nanjing, providing an innovative approach for future exterior window renovations of historical buildings. [ABSTRACT FROM AUTHOR]

This paper presents and discusses results from a field test that uses model predictive control (MPC) to optimise the operation of a multi-source heat pump-based energy system for space heating and hot water provision in a building. The objective of the optimisation is to minimise the electricity consumption of the heat pump using flexibility from the heat sources, the space heating and domestic hot water tanks while ensuring end-user comfort and system constraints. The field test was performed in the context of the RES4BUILD project. The integrated system consists of photovoltaic-thermal (PVT) collectors, an advanced vapour-compression multi-source heat pump and water buffer tanks for space heating and hot water needs. The heat pump utilises a solar buffer, a borehole thermal energy storage (BTES) and air as heat sources. The solar buffer is supplied with heat from the PVT collectors. The optimal operation of the system is obtained by monitoring and controlling the interactions between the different system components using MPC, taking into account the weather and heat load forecasts. Results have shown that MPC has a potential and added value for the optimal operation of multi-source heat pumps in real-life while considering system constraints and user behaviour to ensure thermal comfort. However, significant effort and expert knowledge are needed to develop the sufficiently accurate system models required by this control approach. The outcomes and conclusions of this work are therefore a basis for further development of such control approaches to improve their replicability and feasibility on a large scale, considering the diverse nature of energy system components in buildings. [ABSTRACT FROM AUTHOR]

The rising cost and demand for energy have prompted several organizations to devise innovative methods for monitoring, managing, and conserving energy. Heat energy load prediction in smart buildings is critical for saving energy as well as for meeting energy demands. As the cost of energy and demand is increasing, people must find some way to control it and save energy. To overcome these issues, an intelligent energy management system needs to be developed to help individuals by predicting heat energy demand ahead of time and analyzing energy use patterns. This paper introduces a data-driven short-term heat energy consumption prediction for smart buildings by developing a hybrid deep learning model considering the capabilities of Convolutional and Recurrent Neural Networks. The proposed model extracts hidden patterns from actual data collected through sensors and also uses hand-crafted features to predict the heat energy demand accurately. The efficiency of the proposed model has been evaluated on real smart heat metering data, a case study in Denmark, having three kinds of buildings: single-family, apartment, and terraced houses. The extensive experimental results have been reported with real-world data by considering various evaluation metrics such as MAE, RMSE, MAPE and R 2. The results are promising and outperformed other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A machine learning classifier trained with multiple RTUs predicts faults well. • Converting insignificant faulty cases to unfaulty cases reduced false alarms. • A reduced set of practical features showed good performance. • The classifier's prediction accuracy improved with increasing fault severity. • Performance was poor when applied in the field to a system not used in training. There have been many recent developments to take advantage of machine learning for the task of automated fault detection and diagnosis in air-conditioning equipment. A common approach is to develop a machine learning model to classify fault status, using simulated or measurement data, and test the model's fault diagnostic capability on the same system that was used for training. However, such models don't generalize well to different systems. Compounding this challenge, machine learning requires a rich dataset to train the models, and many of the measurement features require additional sensors, all making the potential for practical machine learning AFDD questionable. The current paper focuses on these challenges by: (i) training a model using simulation data from three rooftop units (RTU); (ii) rebalancing the training dataset to reduce false alarms; (iii) developing a feature set that minimizes sensor cost without significantly impacting performance; (iv) testing the model's performance on a set of laboratory measurement data from the same RTUs that were simulated for training data; (v) testing the model on a faulted RTU in the field. The results demonstrate good progress toward practical application of machine learning based AFDD for RTU, but generalization to different systems remains a challenge. [ABSTRACT FROM AUTHOR]

[Display omitted] • A review of the structural optimization of RCP (radiant cooling panel) was conducted. • Systematically analyzed the status of heat transfer performance parameters of RCP. • Comprehensive introduction and mechanism analysis of RCP structural optimization methods. • The adaptability and engineering prospects of the optimization methods were assessed and justified. Radiant cooling, as an energy-saving, thermal comfort-enhancing, and noise-free air conditioning technology, has garnered increasing attention in recent years. But the risk of condensation and the resulting issue of insufficient cooling capacity becomes prominent when applied in high-heat and humidity regions such as East Asia. Structure optimization of radiant cooling panels is a crucial approach to address these issues, and many scholars have conducted research on the past decade. However, it is rarely comprehensive reports on those optimization methods due to the independently researched and innovated by different paths. This paper provides a comprehensive review of various optimization methods for radiant cooling panels including enhanced heat transfer design, surface treatment, and material selection, and then analyzes the implementation mechanisms, as well as the advantages and disadvantages of its engineering applications. Finally, an analysis of the future development of radiant cooling panels was presented from a practical application perspective. It is intended to support the research on the optimization of radiant cooling panels and the increasing applications of radiant cooling systems worldwide. [ABSTRACT FROM AUTHOR]

Implementing more stringent energy requirements for public buildings has the potential to expedite the renovation rate of building stocks. While many studies have explored optimal renovation solutions to reduce energy consumption and cost, few have established a comprehensive building portfolio strategy from the perspective of portfolio managers. This study addresses this gap by developing a framework that empowers building portfolio managers to make informed decisions regarding building renovations. Through an in-depth case study, the current workflow for determining renovation needs, which involves analyzing maintenance requirements, building usability, and adaptability, is enhanced by incorporating a cost-optimal renovation framework. The study utilizes building simulations and life cycle cost analysis to assess the energy reduction potential of six school buildings requiring renovation. The results demonstrate a potential for energy use reduction, ranging from 11% to 44% when incorporating energy-saving measures into the renovations. Cost-optimal solutions are achieved for two buildings, resulting in energy reduction ranging from 31% to 36% and cost reduction ranging from 7% to 8%. The primary contribution of this research is developing a framework that reduces ambiguities of which buildings a portfolio manager should prioritize for renovation. By identifying current performance and improvement potential, the framework enables benchmarking against energy performance classes, aiding in strategic decision-making. However, the study also underscores the complexities of assessing a diverse building stock from a bottom-up perspective. Barriers such as obtaining high-quality data, scaling assessments, time constraints, and knowledge transfer are problems to overcome. Additionally, evaluating various building typologies with diverse characteristics, including those with legal restrictions due to heritage status, presents unique challenges. Future research directions should focus on expanding the framework to incorporate a life cycle assessment perspective. This approach will ensure a comprehensive building portfolio strategy that mitigates the risk of sub-optimal solutions. • This paper investigates how a municipality can meet stricter energy performance requirements. • A building portfolio framework identifies current and potential energy improvements. • A detailed bottom-up study of a school with six heterogeneous buildings. • Complexities and challenges in municipal renovation decisions are illuminated. • The method prioritizes which building to renovate and improves benchmarking. [ABSTRACT FROM AUTHOR]

Energy communities (ECs) play a crucial role in promoting the adoption of renewable energy resources and establishing local energy markets for sharing and trading energy among end-users (e.g., residential buildings) and prosumers (plus energy buildings). However, since end-users are connected to distribution grids, their energy transactions can sometimes lead to reversed power flows and potential power quality issues, such as over-voltage. This paper introduces the concept of Virtual Energy Communities (VECs) as a coordination mechanism designed to align the goals of ECs with the flexibility requirements of the distribution grid operator. The smart coordination of buildings employs a Particle Swarm Optimisation algorithm to identify optimal VEC configurations that effectively manage potential trading activities to adhere to power grid constraints. The tool's performance is evaluated with a focus on voltage levels and voltage imbalance factors across various case studies. The results demonstrate that smart building coordination efficiently mitigates overvoltage and voltage imbalances by implementing VECs, all while causing minimal disruption to energy sharing opportunities. This is of significant importance, as providing grid flexibility on its own is not economically viable. Therefore, this coordination offers a valuable win-win solution for both ECs and grid flexibility. • Definition of Virtual Energy Communities to solve grid power quality issues. • A novel coordination platform to raise flexibility from residential buildings. • Promoting energy trading between buildings while supporting grid operations. • Performance analysis of the coordination tool for an unbalanced three-phase distribution grid under different scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel radiant heating system with a wall-embedded micro heat pump (WEMHP) for residential buildings. The interior surface temperature of the exterior wall-section empowered by the micro heat pump is independently controlled, enabling distributed thermal comfort delivery. The micro heat pump consists of a miniature compressor along with an aluminum roll-bond condenser and evaporator. In contrast to conventional hydronic radiant systems and heat pumps with separate indoor and outdoor units (i.e., split systems), the WEMHP is a "packaged" system that would not require on-site installation and charging of a secondary water loop and circulation pump or refrigerant piping. This could ultimately lead to reduced installation and maintenance costs. In this study, a detailed system model is presented and integrated with a single-zone building model to assess the energy performance of the WEMHP in heating mode. For a designed living room scenario with an external wall WEMHP-to-wall ratio (HPWR) of 0.6 and window-to-wall ratio (WWR) of 0.2, the simulation results show that up to 23 % energy savings compared with a baseline air-to-air heat pump are possible. The results also indicate the HPWR needs to be higher than 0.4 to achieve performance comparable to the baseline. Additionally, experiments were conducted in a laboratory environment in which surface temperatures were controlled to emulate different scenarios for integration of WEMHP technology and to evaluate thermal comfort conditions. The experimental results show that the median operative temperature differences in the indoor environment were within 0.5 °C for various locations while the largest radiant asymmetry was 2.2 °C for the designed room configuration. Moreover, simulations were conducted to evaluate thermal comfort conditions in applications having a smaller HPWR of 0.4 with various values of WWR. Overall, the combined simulation and experimental study shows that the WEMHP has potential to reduce energy use while maintaining thermal comfort at acceptable levels where the HPWR is higher than 0.4. [ABSTRACT FROM AUTHOR]

The impact of data characteristics on dataset quality is of significant guidance in the field of thermal comfort field surveys. In this paper, a Chinese thermal comfort database has been established, comprising quality-controlled on-site survey data. The database encompasses 41,977 sets of on-site survey data collected from 50 cities across 24 provinces, spanning a period of nearly 20 years from 2001 to 2021. It includes the five thermal zones in China and encompasses three seasons—summer, transitional, and winter—as well as four types of buildings: office buildings, residential buildings, student dormitories, and classrooms. The database exhibits an extensive data distribution, demonstrating a certain level of representativeness in terms of environmental temperature and survey locations. Based on this established database, the thermal sensation model was employed as the criterion for evaluating dataset quality. This study analyzed the impact of data characteristics on dataset qualityfrom three perspectives: sample size, data distribution, and data range. A sample size calculation method using interval estimation was adopted, determiningthat a minimum sample size of 350 is required for on-site thermal comfort surveys in office and residential buildings. A comparison revealed that compared to data with the normal distribution and uniform distribution, data with the positively skewed distribution exhibit lower neutral temperatures, while data with the negatively skewed distribution exhibit higher neutral temperatures. The thermal sensation model constructed based on data with uniform distribution demonstrates greater robustness. As the temperature range decreases, the neutral temperature increases in winter and decreases in summer, resulting in reduced accuracy of the constructed thermal sensation model. [ABSTRACT FROM AUTHOR]

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment. [ABSTRACT FROM AUTHOR]

• The Kalman decay method in suited to multizone's applications. • The Kalman method is 40% more accurate than the 2-points decay method. • The in-situ Kalman decay method has an uncertainty of 20% maximum. • The formulation of the Kalman decay method's uncertainty is given. The measurement of a natural airflow-rate is already challenging. It is often realised thanks to the decay method. Dealing with multi-zone naturally ventilated buildings is even more challenging as multi-zone's applications prevent from using single-tracer gas methods. A method has already shown its ability to measure the global airflow-rate of a multi-zone passive stack dwelling. It is based on the Kalman filter applied to decay methods implemented quasi-simultaneously in each outlet room of a dwelling. This paper presents a numerical sensitivity analysis of the so-called "Kalman decay method". Formulations of the global uncertainty are drawn depending on parameters involved in the analysis. It allowed improving the accuracy, compared to the conventional 2-points method, from 8 to 48%. If the sensor is placed near the outlet, an accuracy between 8 and 20% can be achieved. The main influential parameter is occupation in living rooms of the dwelling during the test, but its effect on the global uncertainty of the Kalman decay method is 20% lower than on the conventional 2-points method. [ABSTRACT FROM AUTHOR]

• A numerical model is established to evaluate the kitchen air quality and thermal comfort comprehensively. • A benchmark experiment of stir-frying shredded potatoes in the kitchen was performed for validation of the numerical model. • The particle concentration maintains high for over 60 s even after the cooking stove is turned off. • The high particulate concentration and dissatisfaction rate usually occur in the upper part of the kitchen. • Increasing the outlet area is more beneficial than increasing the outlet velocity for improving the kitchen environment. This paper presented a numerical model with UDF programming to analyze the performance of the range hood for discharging particulate matter and improving thermal comfort. A benchmark experiment of stir-frying shredded potatoes in the kitchen environment was performed to validate the accuracy and reliability of the present numerical model. Based on the established numerical model, we examined the effects of the ventilation velocity, the outlet area, and their combination on kitchen air quality and thermal comfort. It was revealed that the particulate concentration at two test points reaches the maximum when the cooking stove is turned off. Also, the particulate concentration maintains around the peak regions for over 60 s before it decreases promptly. Moreover, the particulate matter tends to accumulate in the upper part of the kitchen and in positions with poor ventilation. In term of the thermal comfort, the face and shoulder areas of the human body encounter the highest dissatisfaction rate because the heat flow generated by the stove accumulates in the upper part of the kitchen. If the ventilation velocity and the outlet area grow, the temperature difference decreases as well as the air age, the PMV, and the PPD, resulting in better air quality and thermal comfort. Generally, to improve the kitchen environment, increasing the outlet area is more beneficial than increasing the ventilation velocity. The benefits are that the stable performance of removing kitchen particulate pollutants can be achieved, and less energies are consumed. [ABSTRACT FROM AUTHOR]

• We propose a decentralized distributionally robust optimization model for interconnected smart buildings based on P2P energy sharing. • We implement the energy sharing strategy based on the alternating direction method of multipliers. • We develop an equivalent virtual energy storage model for the energy and reserve co-dispatch of HVAC systems. • We apply the distributionally robust optimization approach to accommodate the uncertainty of renewables. The development of distributed renewable energy sources (RESs), and heating, ventilation, and air conditioning (HVAC) systems are facilitating the drive toward smart building energy operation modes, along with peer-to-peer (P2P) energy sharing and reserve co-optimization between smart buildings. However, while distributionally robust optimization (DRO) methods are ideally suited to such decentralized conditions, the application of DRO methods within P2P environments and the co-optimization of operationally flexible HVAC systems for providing additional energy reserves within smart building clusters remain poorly developed. This paper addresses this issue by proposing a decentralized DRO model for smart building clusters. The P2P energy sharing strategy is implemented in a fully decentralized manner based on an alternating direction method of multipliers algorithm, and a virtual energy storage model of HVAC systems is established for implementing the co-optimization of energy and reserve between buildings. Finally, the DRO approach is applied to accommodate uncertainties in RES outputs and thereby obtain a worst-case expected total cost. The DRO model is solved by its reformulation into a tractable second-order cone program by applying linear decision rules and second-order cone duality. The effectiveness and economy of the proposed approach are demonstrated by its simulated application to a representative building cluster consisting of three smart buildings. [ABSTRACT FROM AUTHOR]

The cooling system in airport terminals experiences dynamic fluctuations of cooling load, influenced by factors like outdoor environmental climate and indoor passenger flow. Therefore, establishing a precise and reliable cooling load forecast is crucial for the energy-efficient operation of the cooling system. This paper aims to develop a model for forecasting cooling loads in airport terminals, providing accurate guidelines for energy-saving and optimal operations. Firstly, Singular Spectrum Analysis is applied for data reconstruction. Secondly, a one-dimensional model with a Convolutional Neural Network Algorithm is integrated, replacing the conventional sine and cosine positional encoding. Finally, recognizing the traditional Transformer's limited responsiveness to local features, this study adopts a causal Convolutional approach as an alternative to dot product computation, followed by empirical validation using air conditioning load data from airport terminals. The experimental results demonstrate mean absolute percentage error of 2.16%, 6.39% and 10.71% for single-step, two-step and three-step predictions, respectively, confirming the reliability of the model. It demonstrates superior accuracy compared to various models and provides crucial insights for predicting cooling loads in large public structures with similar data complexity. This model can serve as a solied foundation for guiding the cooling system of airport terminals toward the objectives of energy conservation and emissions reduction. [ABSTRACT FROM AUTHOR]

• The aim of demand-side management is to reduce peak consumption during peak hours. • Low participation of consumers is the vital challenge in DSM. • A flexible DSM method is presented in accordance with consumers' constraints. • The lowest and highest possible consumption of each consumer are considered. • The stability of consumers' total consumption in some time periods is considered. Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. The most important challenge of these programs is the low participation of consumers due to their special constraints. Hence, presenting a flexible demand-side management program that optimizes the consumption pattern of consumers in accordance with their requested constraints will have a significant impact on further participation of consumers in reducing the peak consumption. In this paper, a flexible demand-side management program is presented that provides the optimal consumption patterns, by using the stair-step pricing mechanism and considering the consumers requested constraints. The proposed method is carried out for a combination of consumers with various requested constraints such as the lowest and highest possible consumption per hour and the stability of the total consumption of each consumer in a certain period of time. The simulation outcome showed that the proposed algorithm is able to encourage consumers to participate more in energy management program by meeting their various requested constraints. Peak load total consumption experienced a decline of 25.09% from 36649.4 to 27454.3 kW-hours. As a result, the daily energy cost had been reduced by 10.27 % from 13854.466 to 12,431 dollars. This cost reduction was derived from reducing the need for additional energy generation during peak hours due to transferring consumptions to the lower-priced hours. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two modules were analyzed during Antarctic summer at Spanish research station. • Measured transmittances and infiltrations were excessively high, resulting in high energy consumption for heating. • Indoor air conditions were poor in both modules, particularly the CO 2 concentration. • Extreme weather conditions accelerated the degradation of insulating materials and sealants. • Energy efficiency standards are urgently needed to improve the carbon footprint of Antarctic bases. In this paper, we present the energy characterization of two habitable modules at the Spanish station "Gabriel de Castilla", located on Deception Island, Antarctica. The analysis includes profiles of energy consumption for space heating and domestic hot water, as well as indoor air conditions, which were all recorded during one month of Antarctic summer. Additionally, the measurement of walls' transmittance and air infiltrations, together with thermography analysis, enabled evaluating the conservation state of the buildings' envelope. Upon analyzing all this data, it is evident that there is potential to enhance the design of future modules, improving the thermal comfort while significantly reducing the energy consumption. This will contribute to decrease the logistics and environmental impacts of these research stations, crucial considerations in human activity on the Antarctic continent. [ABSTRACT FROM AUTHOR]

Data-driven models have been widely used in building heating load prediction, but often fail when facing limited data. Previous studies have shown transfer learning can assist model learning of target building under limited data by means of other source building data, however, which is subject to the similarity between source and target building. Selecting similar source building data is not easy, especially when the target building is with limited data. This paper, therefore, proposes a novel meta learning-based framework for building heating load prediction. Using meta learning method, a set of promising model parameters is trained by local and global learning on multiple source buildings data. The obtained model parameters has the ability to get quickly trained with few data in each source building, which is further used as model initialization parameters of target building to assist model learning. Framework validity is confirmed by 550 groups of practical buildings data (50 are as target buildings for testing and 500 are as source buildings). The results showed the proposed framework could reduce the prediction errors by 2.04 %∼61.59 % compared with six common transfer learning methods. The novel meta learning-based framework provides an effective solution for building heating load prediction with limited data. [ABSTRACT FROM AUTHOR]

The earth-to-air heat exchanger (EAHE) is a remarkable technology that taps into the Earth's subsurface heat to either heat or cool the air circulating within buried underground pipes, which can effectively reduce building energy consumption. In cold-arid regions of northwest China, outdoor climate parameters vary considerably throughout the year, and shallow soil temperatures also fluctuate dramatically. This dynamic thermal environment significantly impacts the practical application of the EAHE system. Furthermore, the operation efficiency of the EAHE system can be influenced by the configuration of system parameters. Therefore, this paper takes an EAHE project in Lanzhou as the research object to investigate the operation performance, energy consumption and economy of EAHE in the cold-arid regions of Northwest China. The research results show that heat exchange potential is high in summer and winter, and the outlet temperature varies more gently in winter. During the summer, the cooling capacity of the system is sufficient to the cooling demands of the building, without using auxiliary equipment for cooling; compared to an air-cooled unit with a COP of 3.2, the system reduces cooling energy consumption by 2063.4 kWh. Moreover, we determined the influence degree and optimal scheme of each factor under different evaluation indexes through orthogonal simulation. The specific parameters of the optimal scheme are as follows: pipe length 20 m, pipe diameter 100 mm, buried depth 4 m, and airflow velocity 7 m/s. Compared with the original scheme, the heat transfer efficiency and heat transfer power of this scheme are increased by 37.91 % and 17.6 %, respectively, and the cost per unit heat transfer power is 11,700 CNY/kW, which is 8000 CNY/kW lower than the original scheme. [ABSTRACT FROM AUTHOR]

• A methodology for clustering buildings according to their thermal mass is presented. • Characteristic time constants are defined as flexibility indicators. • The response to different types of demand response events is considered. • A labelling of buildings according to their dynamic response capacity is proposed. • The effectiveness of the methodology is tested on 28 already known real buildings. Demand response programs encompass a range of externally control strategies designed to modify consumer end-use load according to specific grid demands. In the current renewable integration context, power systems need to implement such demand strategies to provide energy flexibility during grid stress periods. Nevertheless, the extensive adoption of demand response initiatives in the building sector is confronted by notable obstacles, mainly due to the absence of standardized assessment methods and metrics, and the lack of established regulatory frameworks, all of which hinder the formation of competitive flexibility asset portfolios. Indeed, energy flexibility quantification frameworks are not unified and are usually based on the control objectives and quantification indicators. In this framework, this paper proposes a methodology to cluster residential buildings based on the analytical assessment of their dynamic thermal response, regardless the boundary conditions (i.e., weather data, occupancies, ...) and the type of demand response event. The proposed methodology provides a quick and simple quantification of how a building is expected to respond under different demand response events and durations, which is critical for both customers and demand response agents to decide and select the involvement of buildings in each event and potentially to design personalized demand response events for each building. An extensive analysis was conducted to evaluate the methodology based on 28 real residential buildings, whose data were presented in a previous study. Results provide the potential effectiveness and application for energy flexibility purposes of this methodology based on dynamic thermal building clustering. Moreover, it can be concluded that it is not possible to deduce a thermal inertia available classification exclusively based on design thermal and geometric characteristics of the building; being necessary to consider the duration of involvement, since they highly influence on the residential building thermal behavior, and thus, on the corresponding clustering. [ABSTRACT FROM AUTHOR]

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities. • Load profiles and electricity consumption datasets are valuable instruments for a better understanding of demand dynamics. • Analysis of appliance behaviour patterns helps to support and optimize turn-key DSM systems. • Controllable appliances' patterns contribute to energy savings and peak load reduction. [ABSTRACT FROM AUTHOR]

Sensor-based data-driven building energy consumption prediction could play an important role in designing and operating energy-efficient buildings. However, existing approaches rely on rigid sensor networks to collect indoor physical parameter data, and the impact of sensor locations on the prediction performance is often poorly understood. To address this gap, this paper aims to study the impact of sensor deployment configurations on building energy consumption prediction, where a configuration is defined in terms of the number and locations of sensors and their flexibility (i.e., fixed or can change over time). Forty-eight (48) configurations were defined and tested. In identifying the sensor locations of the configurations, three clustering approaches were tested. Indoor physical parameter data were collected from an office building using sensors placed at different locations to test these configurations. For each configuration, four feature combinations were also defined to test how well the collected data from the sensor configurations could predict the hourly building energy consumption of three end uses (total, HVAC, and both lighting and receptacles) – alone and in combination with other data (e.g., outdoor weather data). The results showed that the sensor configurations could impact the prediction performance [measured by coefficient of variation (CV)] by 35–76%. The findings from this work could help define sensor deployment configurations for enhanced building energy consumption prediction. [ABSTRACT FROM AUTHOR]

A full-knowledge of the temperature distribution in a data center is crucial for the design and operation to avoid thermal failure. However, it is a great challenge to conduct full-field simulations using the computational fluid dynamics/heat transfer (CFD/HT) method due to the unacceptable computational cost caused by the multi-scale structure of data centers. In this paper, a novel multi-scale model based on POD and CFD coupling is proposed to predict the thermal and fluid characteristics in data centers from room scale to chip scale. The temperature and flow fields in an air-cooled data center are predicted and analyzed under the scales of room, server, IGBT and chip. Based on the CFD results of Room-Server level, 16 sets of design cases' temperature fields for Server-IGBT level are compared using the POD and CFD methods, which shows that the maximum average absolute deviation and average relative deviation are 0.004 °C and 0.59%, respectively. Subsequently, three sets of off-design cases for Server-IGBT level and IGBT-Chip level are predicted using the aforementioned methods, yielding the maximum relative deviation 7.21% and 0.98%, respectively. The salient outcome is the remarkable 175-fold reduction in CPU processing time achieved with the POD model compared to traditional CFD simulations. [ABSTRACT FROM AUTHOR]

Retrofitting the design of a ductwork system is often a critical aspect when renovating a centralized air distribution system. However, the existing approaches rely on the rules of thumb due to the absence of a standardized guideline for the design retrofitting process. Consequently, design engineers face costly and time-consuming challenges in generating retrofitted designs, particularly when dealing with the complexities of reusing the existing ductwork system. Although their design output may achieve functionality, it is not necessarily optimal in terms of cost and performance. To overcome these challenges, this paper proposes a simulation-based ductwork design strategy that utilizes metaheuristic optimization techniques to generate optimized ductwork designs for retrofits. The proposed method offers an automated solution for generating optimized ductwork configurations (i.e., layout and sizes). It strategically maximizes the reuse of existing ductwork components while simultaneously fulfilling the design performance criteria desired by the system and minimizing its life cycle cost. A case study was conducted to present the effectiveness of the developed method in retrofitting the ductwork design for a multi-story school building. This study involved a comparison between the design solution generated by the proposed method and the design solution generated through a conventional retrofitting design approach. The results from the proposed design retrofitting method showed a 17 % lower LCC and an approximately equally well-balanced design. These findings indicate the method's promising potential to contribute to the HVAC industry by providing an effective simulation-based method for automatically optimizing ductwork designs for retrofits. [ABSTRACT FROM AUTHOR]

This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected by a rooftop observatory between November 2021 and April 2022. Over the same period, weather stations were operating at several locations on a university campus in Singapore. From collected data, a method was defined to estimate sensible and latent heat fluxes from building façades, vegetation, and traffic. Before analyzing heat fluxes using the method, thermal images were calibrated against measurements made with contact surface sensors. Results show that the method can be used to study heat fluxes with a higher temporal resolution at the neighborhood scale than any other technique using thermal images collected by a satellite. Heat fluxes can also be analyzed over a longer period while considering urban morphology with a higher fidelity than these estimated using most urban climate models. However, as heat fluxes are directly calculated from measurements, the method is not able to forecast the impact of certain elements in the built environment on urban heat islands. In the future, this limitation can be overcome if heat fluxes assessed by the method are used to train and test data driven models. • Contributors and mitigators of UHIs were studied from thermal images and weather data. • Thermal images were collected using an observatory installed on a rooftop. • A network of weather stations was used to measure outdoor conditions. • Heat fluxes from building facades and vegetation can be assessed from the data. • The data can also be used to observe heat releases from traffic. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to predict the occupancy for building energy systems (BES). A Gaussian Process (GP) is used to model the occupancy and is represented as a state space model that is equivalent to the full GP if Kalman filtering and smoothing is used. The combination of GPs and mechanistic models is called Latent Force Model (LFM). An LFM-based model predictive control (MPC) concept for BES is presented that benefits from the extrapolation capability of mechanistic models and the learning ability of GPs to predict the occupancy within the building. Simulations with EnergyPlus and a comparison with real-world data from the Bosch Research Campus in Renningen show that a reduced energy demand and thermal discomfort can be obtained with the LFM-based MPC scheme by accounting for the predicted stochastic occupancy. [ABSTRACT FROM AUTHOR]

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R 2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability. • The proposed LSTM model forecasts accurate and reliable short, medium, and long-term energy consumption predictions. • The proposed LSTM model is capable of energy consumption predictions in both residential and commercial buildings. • The proposed LSTM requires a minimal amount of training data to forecast accurately and uses a variety of features including weather and occupancy data for precise energy consumption prediction. • The model achieves an average R2-score of 0.91 and average MAE of 0.013. [ABSTRACT FROM AUTHOR]

• An improved beluga whale optimization is proposed for load distribution of chillers. • The optimization accuracy, convergence speed and robustness are enhanced in IBWO. • Compared to conventional method, IBWO has significant energy savings for OCL problem. • The utilization of IBWO is more energy efficient at lower refrigeration demand. A large part of the total energy consumption of building operation comes from chillers system in the building refrigeration system. Optimizing the load distribution of every chiller unit is able to significantly decrease the power consumption of the system. Therefore, this paper proposes an improved beluga whale optimization (IBWO) using multiple strategies addressing the optimal chiller loading (OCL) problems. IBWO incorporates circle chaotic mapping, the efficient search operator of producers of sparrow search algorithm (SSA), and Cauchy mutation strategy to enhance global optimization capability, convergence, and robustness. The enhanced algorithm performance was validated through testing with 6 benchmark functions using MATLAB, demonstrating its improved effectiveness. Additionally, IBWO is applied to the power consumption optimization and load distribution of two typical chiller systems. The results illustrate that compared with the conventional method and other meta -heuristic algorithm, IBWO can provide an energy-saving scheme with excellent robustness, less power consumption and higher overall refrigeration efficiency in a short number of iterations, which preliminarily proves the feasibility for dealing with OCL problems. [ABSTRACT FROM AUTHOR]

The residential building sector accounts for a significant portion of global energy consumption, necessitating energy-efficient building practices to mitigate climate change. The metric most used in legislation and building codes to promote energy conservation is Energy Use Intensity (EUI). The efficacy of such regulation is often taken for granted, but as this study of a representative sample of over 800 households in Israel demonstrates, energy 'efficiency' is not necessarily translated into lower total consumption. Multiple linear regression and path analysis were employed to identify significant direct and indirect factors shaping electricity consumption measured for the first time by all three metrics: total consumption, per capita consumption, and EUI. Factors considered included building properties as well as occupant demographic characteristics, environmental awareness, and lifestyles. The factors found to be of greatest importance were, in descending order, house floor area, number of occupants, and building type (detached single-family or multi-family apartment). Other factors, including income, were shown to have an indirect effect. EUI was found to be an ineffective metric, because it allows greater consumption in larger apartments with the same efficiency rating for climatization as smaller but otherwise similar ones. In our sample, EUI for climatization decreased by about 0.14 kWh/m2/year for every 1 m2 increase in floor area. To address this, the research proposes implementing stricter efficiency requirements for large and detached houses based on a novel 'size-normalized EUI'. In addition, to promote behavior change in a socially equitable way the paper proposes the introduction of tiered electricity bills based on consumption levels adjusted for the number of household occupants. [ABSTRACT FROM AUTHOR]

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]

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]