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• Three-pulses approach (ASHRAE-Tp8) for sizing ground-coupled heat pump systems. • Comparison of monthly and three-pulses approaches for estimating borefield length. • Hourly heat load profiles for different European countries exploiting EnergyPlus. • BHEDesigner8 is the only available free web app for sizing geothermal probe fields. • Borefield length by BHEDesigner8 with 5% accuracy compared to commercial codes. The European targets addressed to greenhouse gas emissions reduction require a series of actions, including the role to be played by heat pumps and ground-coupled (GCHP) high performance ones. The correct design of the Borehole Heat Exchangers (BHE) field is the core of any GCHP project. The building heating and cooling demand is one of the inputs that shape the problem, to be associated with the use of precalculated temperature response factors describing the ground thermal response to different BHE field arrangements. Commercial codes (e.g. EED, GLHEPRO) can address such a complex computing task by referring to the monthly step thermal loads. In this paper, the monthly approach is compared with the three thermal pulses approach (modified ASHRAE-T p8 method) by investigating a series of typical building structures in Europe (Tabula webtool database). Hourly heat load profiles for different European countries (Italy, France, Sweden, Germany, Spain) are calculated by EnergyPlus simulations. The present analysis, based on a representative set of building heat demands in Europe, demonstrates that the simple three-pulses approach with the T p8 formulas is able to provide the correct BHE field overall length with 5% accuracy (average error 2.22%) with respect to the reference monthly calculations performed with commercial codes. [ABSTRACT FROM AUTHOR]

Although buildings are transitioning towards complex, dynamic, and interconnected systems, traditional engineering metrics that dominate today don't capture several important whole-network properties. To address this, this paper adopts ecological network analysis (ENA), which has numerous successes for natural ecosystems and socio-technical systems but has yet to be applied for buildings. After translating ENA into comprehensive mathematical models for engineering applications, the novel ENA method is demonstrated with building and community energy systems. For the models to suit building energy systems, which have multiple energy types and non-negligible dynamics, ENA is formulated on an exergy basis, with dynamic flows and balances (i.e., time-varying storages). To demonstrate the proposed approach, ENA is used to redesign the heating and cooling systems for an office building and data center, coupling the buildings together via ambient-loop district energy. The models are implemented using the equation-based Modelica language. Results indicate that the ENA-guided redesign reduces the source energy by 15%. The energy consumed by the heating and cooling systems is reduced by 84% with negligible sacrifice to the thermal performance. Surprisingly, the redesign also reduced the exergy efficiency of the total system from 60% to 34% due to a greater decrease in exergy output relative to exergy input with low-exergy system designs. This indicates that ENA and other network approaches that classify system organization may outperform traditional efficiency-based metrics for building and community energy systems when whole-system perspectives are desired. • The first to apply ecological network analysis (ENA) for buildings. • Proposes exergy is best for ENA of dynamic systems with multiple energy types. • Demonstrates how ENA adds novel complex network information. • Case study with office and data center achieved 84% heating/cooling energy savings. • Reduced exergy efficiency in top design shows value of new network-based analysis. [ABSTRACT FROM AUTHOR]

• A two-level control strategy for energy efficient VRF operation with FR service. • The proposed strategy has much lower modeling requirement over model-based methods. • Low level: robust sliding mode control with ODU fan power tracking the FR reference. • High level: model-free real-time efficiency optimization with constrained ESC. • Tuning compressor discharge pressure constrained with zone temperature regulation. Heating, ventilation and air conditioning (HVAC) systems of buildings are abundant sources for the Frequency Regulation (FR) ancillary service, whose actuating devices are controlled to follow the real-time power reference signals with the contractual capacity. For HVAC practice, model-based FR controls are hindered by expensive model acquisition. This paper presents a two-layer control strategy for HVAC systems to perform energy efficient FR service, with a rather low model requirement. At the lower level, the FR operation with a selected actuator is realized with a sliding mode control which is robust to power map variation. At the higher level, system efficiency is optimized in real time via a model-free constrained extremum seeking control (CESC). A conceptual variable refrigerant flow (VRF) system is used as the illustrative application, for which the outdoor unit fan offers the FR power reference tracking with SMC. At the outer loop, the compressor discharge pressure is adjusted by a CESC to minimize the total power consumption while the zonal temperature regulation is reinforced as equality constraint. The proposed strategy is evaluated with simulations on a Modelica simulation, and the results validate its effectiveness. The Net Energy Cost can reduce by 15% to 23% under different load levels. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC)-based precooling strategies are typically analyzed through simulations. Their field studies in residential buildings are quite limited due to hardware retrofitting costs, lengthy parameter identification periods, and the need to have homeowners' participation. Therefore, there is a lack of MPC-based precooling impact analysis using field data which may have prevented widespread adoption of the technology. To address this issue, in this paper we developed an MPC agent and implemented it on an online cloud-based platform. The MPC agent makes use of a gray-box model to capture the home thermal dynamics, a novel set of constraints to ensure that the model parameters identified are realistic, and a node sensor to provide interior wall surface temperature measurement. To evaluate its performance and conduct impact analysis, we introduced a modified matrix profile-based weather clustering algorithm and two performance indicators for cost saving and energy flexibility. We also carried out extensive field tests on nine homes over a period of four months. Our results show that the MPC agent can reduce energy cost by 28.72%–51.31% on hot summer days and by up to 60.32% on mild summer days, in addition to achieving significant energy flexibility. Moreover, the agent's performance is found to be most impacted by weather conditions, AC performance, user comfort preferences, and floor areas of the homes. [ABSTRACT FROM AUTHOR]

Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants. [ABSTRACT FROM AUTHOR]

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures. • Utilization of ensemble-based machine learning modeling. • Implementation of an end-use demand segregation. • Identification of key building features for each end-use demand prediction. • Analysis of the impact of retrofit measures using future weather data. [ABSTRACT FROM AUTHOR]

The buildings sector is responsible for 37 % of global final energy consumption and nearly 40 % of total direct and indirect CO 2 emissions. This has led to promoting renovation efforts to decrease operational emissions of the existing building stock. With decreasing operational emissions, embodied emissions are becoming more important. In new buildings, embodied emissions account for about half of total emissions through the life cycle of a building. If both embodied and operational emissions are considered, renovation often outperforms new constructions, due to high embodied emissions of the structure - especially solid construction with a basement. Based on this, the decision if a building should be renovated or replaced is often not straight forward and depends on various factors. In this paper, a typical German building was considered as a case study, for which both renovation and replacement scenarios were considered, to identify the optimal solutions in terms of overall energy consumption and CO 2 emissions of the building in the use phase, the environmental impacts of the building along its entire life cycle, and related costs. Results show that the lowest CO 2 emissions during the lifetime of the analyzed building can be achieved with a sustainable replacement building (-2.05 kg CO 2 /m2/year) by using a heat pump with ground collector coupled with PV. This allows, compared to the existing reference building, reductions of 97 % and 101 % in terms of energy consumption and CO 2 emissions, respectively; while natural gas-based technologies are the least targeted and the most volatile to fuels' prices changes over the years. [ABSTRACT FROM AUTHOR]

Energy optimization leveraging artificially intelligent algorithms has been proven effective. However, when buildings are commissioned, there is no historical data that could be used to train these algorithms. On-line Reinforcement Learning (RL) algorithms have shown significant promise, but their deployment carries a significant risk, because as the RL agent initially explores its action space it could cause significant discomfort to the building residents. In this paper we present ReLBOT – a new technique that uses transfer learning in conjunction with deep RL to transfer knowledge from an existing, optimized and instrumented building, to the newly commissioning smart building, to reduce the adverse impact of the reinforcement learning agent's warm-up period. We demonstrate improvements of up to 84% (40% average) in the duration, and up to 99% (62% average) in prediction variance, for the reinforcement learning agent's warm-up period. RL agent's prediction accuracy improved by up to 4% (1% average). • The first similarity-informed transfer learning method to implement reinforcement learning for building energy optimization. • Warm-up period duration reduced by up to 84% (40% average). • Prediction variance reduced by up to 99% (62% average). • RL agent's prediction accuracy improved by up to 4% (1% average). [ABSTRACT FROM AUTHOR]

[Display omitted] • A blockchain and ML-based energy management framework for multiple buildings. • Multi-types of energy (i.e., heat, cold and electricity) allocation and transmission. • Machine learning to predict day-ahead patterns of energy generation and consumption. • P2P energy trading among prosumers and consumers within the multi-energy network. • Optimal and automated energy allocation among multiple energy users through PSO. In most domestic buildings, gas and electricity are supplied by energy and utility companies through centralised energy systems. This often results in a high burden on central management systems and has adverse effects on energy prices. Blockchain-based peer-to-peer energy trading platforms can deliver strategic operation of decentralised multi-energy network among multiple domestic buildings to reduce global greenhouse gas emissions and address global climate change issues. However, prevailing blockchain-based energy trading platforms focused on system implementation for peer-to-peer electricity trading while lacking predictive control and energy scheduling optimisation. Therefore, this paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy (i.e., heat and electricity) allocation and transmission, among multiple domestic buildings. Machine learning is harnessed to predict day-ahead energy generation and consumption patterns of prosumers and consumers within the multi-energy network. The proposed blockchain and machine learning-based decentralised energy management framework will establish optimal and automated energy allocation among multiple energy users through peer-to-peer energy transactions. This approach focuses on energy-matching from both the supply and demand sides while encouraging direct energy trading between prosumers and consumers. The security and fairness of energy trading can also be enhanced by using smart contracts to strictly execute the energy trading and bill payment rules. A case study of 4 real-life domestic buildings is introduced to determine the economic and technical potential of the proposed framework. In comparison to prevailing approaches, a key benefit from the proposed approach is an improved computational load/failure of a single point, energy trading strategy, workload, and capital cost energy. Findings suggest that energy costs reduced between 7.60% and 25.41% for prosumer buildings and a fall of 5.40%-17.63% for consumer buildings. In practical applications, the proposed approach can involve a larger number of prosumer and consumer buildings within the community to decentralise multiple energy trading, thus significantly contributing to the reduction of greenhouse gas emissions and enhancing environmental sustainability. [ABSTRACT FROM AUTHOR]

[Display omitted] • Frosting & defrosting characteristics of appliances are reviewed from 2000 to 2023. • Parameters affecting frost formation in 'no-frost' evaporators are surveyed. • Methods for improving frosting and defrosting performance are summarized. • Correlations of j and f factors for 'no-frost' evaporators are summarized. • Major findings and recommendations for future research are presented. Refrigerators and freezers are now common household devices, and they consume a considerable amount of energy each year. The deposition and accumulation of frost on the surface of the refrigerator evaporator is inevitable and always has a significant negative impact. The periodic defrosting is necessary for household refrigerators, which increases the energy consumption and the compartment temperature. This paper presents the key findings concerning the frosting and defrosting characteristics of refrigerators and freezers based on the existing literature in 2000 ∼ 2023. In the first part, the frosting research is summarized. Firstly, the parameters affecting the frost formation of the refrigerator evaporator are analyzed. Then, the structure of the 'no-frost' evaporator is introduced. Meanwhile, the heat transfer and pressure drop characteristics of the 'no-frost' evaporator are discussed. Two methods for improving 'no-frost' evaporator performance are also presented. The second part focuses on the defrosting research, including an analysis of defrosting energy consumption. Four methods to improve refrigerator defrosting performance are also summarized. Suggestions are made for future research initiatives and essential improvement measures. This review strives to serve as a valuable reference for the study of frosting and defrosting characteristics in household refrigerators and freezers. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

• Climate change's impact on retrofitting was tested on Le Corbusier's apartment. • Climate change scenarios project up to 70% increase in summer overheating. • Adaptive natural ventilation can reduce warm discomfort of 50–60%. • Adaptive setpoints can reduce total energy consumption of 20–30%. • Adaptive strategies are particularly favourable in historic buildings. Historic buildings, often exempt from retrofits to preserve originality, require retrofitting due to potential future climate-related indoor issues. For Modern Architecture constructions, there is a need to find solutions that help address the discomfort and heat losses caused by the extensive use of glazing. By analysing Le Corbusier's studio-apartment this paper aims to inspect: (i) Solutions for energy, Indoor Environmental Quality, and preservation; (ii) Climate change impact on them; (iii) Possible adaptation strategies to cope with climate change. Spectrally selective glazing reduced summer overheating by 15%; high-performance glazing lowers cold discomfort (24%) and heating (22%) but increased warm discomfort; shadings maintained energy use and reduced summer discomfort by up to 44%; thermal insulation can reduce winter discomfort and halves energy use but raised summer discomfort by up to 41%. Tests on 2050–2100 climate change scenarios reveal up to 70% more warm discomfort hours. Adaptive strategies can help, with natural ventilation cutting warm discomfort by 50–60%, and adaptive setpoints reducing cooling demand by about 35%. Adaptive strategies can complement traditional retrofitting in addressing current and future climate conditions, especially in historic buildings that require minimal interventions to preserve their historical characteristics and enhance resilience. [ABSTRACT FROM AUTHOR]

• An archetype-based bottom-up modelling approach for building clusters is proposed. • A flexibility signature method is proposed to characterize energy flexibility. • 22–27% peak demand reduction is achieved with two different control strategies. • An economic return of over $40,000 is obtained based on a demand response program. The electric grid faces increasing challenges in the context of electrification and renewable energy integration, gradually evolving into a smart grid. Buildings could facilitate this energy transition by providing flexibility services to the grid. Existing research on building energy flexibility has mostly focused on single buildings due to the difficulty of modelling buildings at the urban scale. This paper adopts an archetype-based approach to model generic building clusters, targeting various building types, compositions, and cluster sizes. We applied this approach to a case study cluster of 54 buildings. With the cluster model, we then characterized its operational flexibility associated with two rule-based room temperature controls. Simulation results show that the building cluster achieves 22–27% peak demand reduction on average against its regular operation throughout a series of flexibility events. We also evaluated the financial benefits of the flexibility service in an incentive program from a Canadian utility; an economic return of over $40,000 per winter was obtained for the building cluster. This work demonstrates the operational energy flexibility value in building clusters, benefiting not only the grid operator but also building owners. [ABSTRACT FROM AUTHOR]

• A novel ORC-VCR system configuration is proposed. • Thermodynamic, economic and environmental analysis of the system is conducted. • Improvement in thermodynamic performance of new system exceeds worsen in economic performance. • The effects of recuperator effectiveness and flash pressure ratio are analyzed. • Different positions of phase transition temperature is compared. The improvement of system configuration is an effective method to improve performance of the organic Rankine cycle-vapor compression refrigeration (ORC-VCR) system. A novel system with a recuperator on the ORC side and a flash tank on the VCR side is proposed. Firstly, the theoretical model of thermodynamic, economic and environmental performance is constructed. Then, the novel system is compared with the basic ORC-VCR system. Next, the effects of operating parameters on performance of the proposed system are investigated. Finally, a comparison of system performance in different positions of phase transition temperature is carried out. The results show that the coefficient of performance(COP), exergy efficiency, dynamic payback period(DPP), net present value(NPV) and annual emission reduction(AER) of the novel system are 0.38, 5.94 %, 8.89 years, 1.52 × 106 $ and 5.77 × 106 kg, respectively. Compared with the basic ORC-VCR system, the improvement in thermodynamic performance of the novel system is relatively preferable to the deterioration in economic performance. Moreover, the effect of mixture composition on system performance is distinct in different positions of phase transition temperature. It is shown that using zeotropic mixture is not appropriate in all cases. This paper can provide theoretical guidance to further improve performance of the ORC-VCR system. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in electricity consumption and carbon emissions from households in smart buildings. This increase has had a profound impact on the economic aspects of residential electricity consumption and the achievement of the national dual carbon goal. In this paper, an optimal scheduling model for household loads in smart buildings is established. It considers the user demand side, grid-side supply, and the impact of the PV energy storage system (PESS) on carbon emissions. The optimization objectives are the economy of household electricity consumption and the total deviation of electricity consumption. Second, the adaptive mixed inverse learning strategy multi-objective beluga optimization algorithm (AMOBWO) is adopted to enhance its overall dynamic optimization. This optimization addresses challenges such as the uneven distribution of electricity consumption, load timing constraints, power limitations, and uncertainty in electricity consumption during the scheduling of smart building loads. Finally, the results demonstrate that AMOBWO decreases the total cost of household electricity consumption in smart buildings by 59.32% (based on the selected maximum PV capacity). Additionally, it reduces the total deviation by 14.28% after optimal scheduling. As the PESS capacity increases, there is a significant reduction in the indirect carbon emissions from smart buildings, decreasing from 881.279 kg CO 2 to 431.299 kg CO 2. [ABSTRACT FROM AUTHOR]

The increase in the uptake of distributed energy resources (DERs) makes the distribution network more complex. Inadequate management of these DERs may lead to escalated power transmission, potentially resulting in challenges within the distribution network and provoking degradation of critical assets like distribution transformers. The smart home has a home energy management system (HEMS) to schedule the DERs at the home level based on the objectives of the end-user. However, HEMS is not coordinated with the distribution network operators, which can cause different network issues at the distribution level. To mitigate this, effective management of DERs across different levels of the distribution network is essential. This paper introduces a four-stage optimization within a three-level coordination framework that aims to maximize benefits for all stakeholders. A mixed integer linear programming (MILP)-based HEMS is formulated in the first stage to perform home energy management effectively. At the aggregator level, in the second stage, a MILP-enabled peer-to-peer (P2P) trading mechanism is designed. At the same level, a third-stage loss of life optimization is performed pertaining to the optimal power status of the HEMS and P2P trading. In the last stage, a three-phase optimal power flow-based optimization is proposed to maintain the operational constraints of the unbalanced distribution network. Optimizing HEMS and P2P trading while addressing transformer limitations, our proposed method reduces peak power by 38.85%. The transformer life loss using P2P is only 0.00059%, while in HEMS, it is 0.62% per day. Additionally, our method substantially lowers electricity costs for P2P prosumers. Thus, our proposed method outperforms other existing mechanisms from both financial and physical network operation suitability perspectives. • Developing a new home energy management system. • Developing a network-aware MILP-based peer-to-peer trading model. • Analyzing impacts on distribution transformer. • Analyzing impacts on the three-phase unbalanced distribution network. [ABSTRACT FROM AUTHOR]

[Display omitted] • A mathematical model of droplet evaporation under convection heat transfer is established. • The characteristics of spray droplet particles are analyzed. • The spray pressure is one of the key factors affecting droplet evaporation. • The thermal environment in the tunnel would affect the spray cooling effect. • The reasonable layout of nozzles can effectively improve the temperature and humidity in the tunnel. Spray cooling technology is an effective and sustainable cooling method that is frequently used in hot environments, both indoors and outdoors. This paper studied the theory of droplet evaporation under convection heat transfer and established a model of tunnel construction under high-temperature. The results of the field measurements were used to determine the boundary conditions for the model. After that, the cooling effect under various spray conditions (spray pressure P n and water temperature Tw) was explored. Next, it was discussed how the tunnel's thermal environment affected the droplet evaporation, and a suitable layout of nozzles was chosen. The results showed that, firstly, the droplet group produced by spraying was in an irregularly cone-shaped distribution, with most droplets falling within the particle size range of 30–40 μm. Secondly, when the spray pressure (P n) was increased, the break-up of droplets was better, which would improve the evaporation flow of droplets. However, the evaporative ratio of the droplets exhibited a peak value. When P n = 4 MPa, the droplet evaporation ratio reached its maximum at 82.7 %. Thirdly, in a high-humidity environment, the evaporation ratio of droplets would decrease, and the average evaporation time of a single droplet would increase, leading to the reduction of the spray cooling effect. When D increased from 5 g/kg to 25 g/kg, the average evaporation time rose at 5.7 s. Fourthly, when constructing in the tunnel's high-temperature zone, the number of nozzles (N) could be raised to achieve the cooling effect; and the cooling effect was optimal when the spacing between nozzles (Δx) was 20–30 m. The research results enriched the study of spray parameters and thermal environments on the cooling effect of droplet evaporation, which could provide a reference for applying spray cooling technology in the tunnel. [ABSTRACT FROM AUTHOR]

Personalized thermal sensation models play a crucial role in ensuring occupant's thermal comfort satisfaction and improving building energy efficiency. However, an adaptive and accurate personalized model that can be easily implemented in real life is still challenging. This paper investigates the influencing factors of thermal sensation vote (TSV) and proposes a personalized regression model that only uses a single local skin temperature as the key indicator. A survey is conducted with forty subjects aging from 20 to 59 years old. The relationship among ambient temperature, skin temperatures, and subjective TSV is analyzed. The forehead temperature is recommended as the key indicator for prediction because it exhibits a strong correlation with ambient temperature and TSV, and it is easy to capture. Furthermore, the impact of individual characteristics on TSV is investigated. The proposed model effectively captures and compensates for individual differences by incorporating subjects' set point skin temperature and body fat percentage (BF%). The proposed model can be readily applied in real-life scenarios due to its minimal requirement for occupant's feedback and its higher accuracy compared to other models. Specifically, it exhibits a significantly lower Root Mean Square Error (RMSE) of 15.8 %, 9.4 %, and 65.2 % compared to the Support Vector Regression (SVR) model Zhang's model and Zhou's model. Moreover, the proposed model showcases the lowest mean absolute error among the compared models. This approach of developing a personalized regression model based on local body temperature holds promise for future international ergonomic standard development. [ABSTRACT FROM AUTHOR]

• MPC strategy applicable to the operation of solar heating system was established. • The utilization of heat collection and storage was maximized by changing operation strategy. • The advantages and disadvantages of MPC were obtained. The operation performance of a solar heating system is largely influenced by the coupling of outdoor climate conditions and building energy operation patterns, and is characterized by strong randomness and large heating fluctuations, which makes the optimal control rules for solar heating systems in dynamic state. Although the traditional RBC(rule-based control), whose rules is preset, has a fast response speed, the fixed-rules are hardly to make the solar heating system in optimal state. Furthermore, solar heating systems is generally characterized by a large time delay in the heat transfer process. The control point lag occurs in the control signal of the dynamic control strategy. To address these issues, this paper proposes a model predictive control-based (MPC) control strategy for solar heating systems. The method uses seq2seq-LSTM to predict major operating parameters including ambient temperature, solar radiation, the load inside the building for the next 24 h and combines the heating system operation model to simulate and obtain the control signal at the next moment. The results showed that, compared with traditional control strategies including RBC and PID control strategies, the efficiency of solar collector regulated by MPC increased by 9%. Taken the tank temperature at the simulation end as the energy calculation reference point, the solar heating system's total energy consumption employing MPC is 34.2% lower than that employing traditional control strategy. [ABSTRACT FROM AUTHOR]

[Display omitted] • The paper offers an overview of the implementation of key building energy efficiency requirements in Member States. • The renovation rate has to further increase to achieve zero-emission buildings. • The EU building stock has the potential to achieve a remarkable 94% reduction in direct greenhouse gas emissions by 2050. • Member States strongly rely on renewable technologies deployment for achieving climate neutrality. • Average energy performance level of Nearly-Zero Energy Buildings (NZEB) lags behind EU benchmarks. The European Union (EU) is accelerating its clean energy transition in order to reach climate neutrality in 2050 through targeted policies, with the Energy Performance of Buildings Directive (EPBD) playing a pivotal role in reducing buildings energy consumption. This study evaluates the progress of EU Member States in implementing the EPBD requirements, focusing on Long-Term Renovation Strategies (LTRS), Nearly-Zero Energy Buildings (NZEB), Energy Performance Certificates (EPC), and technical building system inspections. Harmonized data enable cross-country comparisons, revealing trends across climatic zones and assessing policy impacts at the EU level. Aligning renovations with 2020 LTRS could cut greenhouse gas emissions by 35% by 2030 and 94% by 2050. Despite the well-established status of NZEB, their energy performance lags behind EU benchmarks in all climatic zones. Moreover, diverse EPC methodologies complicate in-depth comparisons, yet nearly 70% of EU certification schemes define energy classes based on an energy indicator. Finally, over 75% of Member States implemented heating and cooling system inspection schemes, but few assess their impacts. While progress towards a highly energy-efficient building stock is evident, challenges persist. Member States must intensify efforts, especially in increasing the renovation rate with the aim of achieving zero-emission buildings. The recast EPBD, nearing adoption, will assist Member States by imposing enhanced requirements for building decarbonisation. This effort holds potential to unlock co-benefits, including climate change mitigation, energy resilience, and improved life quality. [ABSTRACT FROM AUTHOR]

• A thermal-textured BIM generation framework for UAV-enabled building energy audit is proposed. • A low-cost calibration target that exhibits high precision and ease of production. • A thermal image-to-BIM registration method by multi-source image fusion. • A histogram-based approach to correct thermally inconsistent phenomenon and enhance image contrast. • A high-contrast thermal-textured BIM of a pre-1920 masonry building is generated. Mapping Unmanned Aerial Vehicle (UAV)-based thermal images of building façades onto a Building Information Model (BIM) can greatly support building energy audit. However, accurately registering these images into BIM is challenging due to thermal image's unique characteristics of low texture and high distortion. To address these issues, this paper proposes a new multi-source image fusion framework for registering UAV thermal images to BIM. First, a low-cost target is designed and produced using 3D printing for calibrating UAV thermal camera. The rectified thermal images are then fused with RGB images through Homography transformation, enabling thermal images to be registered accurately with BIM. Additionally, a histogram-based approach is proposed to correct the thermally inconsistent phenomenon and enhance the contrast to produce high-contrast thermal-textured BIM. Field tests confirmed a mean image fusion error of around 5.713 pixels. A case study on a masonry building demonstrated the feasibility and effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

The fault diagnosis of chillers is of great significance in reducing building energy consumption and extending the operational lifespan of refrigeration equipment. Several popular machine learning-based fault diagnosis methods rely heavily on many labeled samples. However, such samples are difficult to obtain in practice due to sparse fault data and high labeling costs. This limits the application of ML-based fault diagnosis methods based on supervised learning. To reduce the dependence on labeled samples, this paper proposes a novel chiller fault diagnosis method based on neighbor-optimized graph convolutional network. The method improves the utilization efficiency of unlabeled samples by mining the spatio-temporal relationship between a large number of unlabeled samples and a limited number of labeled samples. And it dynamically adjusts the graph's structure by optimizing the number of adjacent samples in the correlation graph to obtain better diagnostic results. Its effectiveness is validated on the authoritative dataset ASHRAE RP-1043 and a more challenging dataset of real-world chillers in a building. Experimental results show that the proposed method can achieve better diagnostic performance than the state-of-the-art methods. • The semi-supervised fault diagnosis method for chillers based on neighbor-optimized graph convolutional network is proposed. • A neighbor optimization method is proposed to optimize the neighbor samples aggregated by GCN. • The effectiveness of this method is verified on the authoritative dataset and the more challenging dataset. • Compared with the state-of-the-art methods, the performance of the proposed method is superior. [ABSTRACT FROM AUTHOR]

• Importance of solar energy for generating clean power using PV panels. • Emphasizing the need to review solar simulation tools for its usability. • Validation of system-based and online software tools for solar energy simulation. • Validation using real-world data applying statistical metrics for accuracy. • Providing valuable guidance for selecting appropriate PV simulation tools. Solar energy is being recognized around the world for generating clean power using photovoltaic (PV) panels. Inadequate planning and sizing of PV panel systems leads to increased costs, emphasising the significance of reviewing solar energy simulation tools for accuracy and credibility. Even with the current literature, it could be challenging to select the software tools that are appropriate for the research. This paper identified, reviewed and validated four system-based (System Advisor Model (SAM), PVsyst, PV*Sol and Solarius PV) and three online software tools (Helioscope, PV-Watts and PV-GIS). It provides necessary information on the software tools which compiled in a features matrix under seven categories. The validation process involves using real-world data from an 84.5KW solar plant installed on a building in Nagpur, India, to assess the accuracy and performance of the software tools. The statistical metrics used for validation are Absolute Deviation (AD), Root Mean Square Error (RMSE) accompanied by Mean Bias Error (MBE) and Nash-Sutcliffe Efficiency (NSE) for a comprehensive understanding of the accuracy and reliability. The findings show a discrepancy in the simulated output data and the recorded data of the tools with PV*Sol and Solarius PV showcasing the least error. In conclusion, the findings of this research provide valuable insights and guidance for users seeking to make informed decisions in selecting the most appropriate Solar PV simulation tool tailored to their specific needs and requirements. [ABSTRACT FROM AUTHOR]

This paper implements a machine learning(ML)-based procedure for constructing the missing sensor(s) data in a net zero energy building in case of complete failure in data recording (for up to one hour). In the first scenario, missing temperature data is re-created using the sensor's ex-ante data, the HVAC system's status flag, and the ambient conditions. In the second scenario, the temperature data (until failure occurred) from two close-by spaces are also utilized as inputs. For each scenario, ML-based pipelines' performance is first assessed by considering different prediction horizons using a benchmark algorithm. Next, each pipeline's most promising features and the most suitable algorithm are identified. Using the obtained optimal pipeline, a sliding window-based training scheme is implemented, and the size of the training window is optimized. It is shown that feature selection, algorithm optimization procedures, and the sliding window-based training scheme notably improve the forecasting performance. The proposed methodology can be deployed as a tool in intervals with total data logging failure, providing data to ML-based controllers in smart buildings and avoiding disruptions in the building management system. • A method for handling short-term Data Logging Failure in Smart Buildings is proposed. • Machine learning-based pipelines for recreating missing sensor data are developed. • The most promising features and algorithm for each prediction horizon are identified. • A sliding window-based training scheme is utilized, and optimal window size is found. • The impact of adding data from nearby sensors on the achieved accuracy is explored. [ABSTRACT FROM AUTHOR]

[Display omitted] Users' adaptive behaviors significantly affect the indoor environment and energy use. Neglecting them would introduce considerable uncertainty in the computer simulation results and lead to suboptimal or even invalid energy-efficiency solutions in optimization. To address this issue, we abstracted and simplified three user behavior patterns, including adjustment of operable shading devices, switching on/off artificial lighting, and opening windows for natural ventilation by a brief literature review. We then developed a new simulation approach that incorporates these behavior patterns and applied it to optimize the exterior fixed solar shade design of a south-facing office room in Qingdao as an example. The baseline model was established and validated in both daylight and energy simulations. The results demonstrate that the addition of user behavior models has a significant impact on both illuminance and load simulation results. The evaluation of each shading solution under the traditional approach and the newly-developed approach shows considerable differences, leading to the selection of different optimal solutions. Ultimately, the 0.6-meter-long type 8 (horizontal sunshade combined with vertical triangular panels on both sides) and 0.7-meter-long type 4 (double-sided vertical shading) were selected as the optimal solution using the traditional approach and new approach, respectively. This research offers valuable insights into the user behavior pattern and uses a shading optimization example to demonstrate the significant impact of incorporating adaptive behavior mechanisms on simulation and optimization results. The approach established in this paper has the potential to be extended to other solutions, being conducive to the study of refined building efficiency strategies for different conditions. [ABSTRACT FROM AUTHOR]

• A metodology for evaluating comfort temperature ranges is carried out. • New evaluation metrics regading thermal comfort and energy savings are proposed. • Quantitative evaluations of modifying thermal comfort ranges are provided. The selection of a suitable indoor temperature is a challenge to society, since its relationship with thermal comfort and energy consumption. The adaptive thermal comfort theory addresses this problem relating indoor with outdoor temperatures and based on that, defines temperature ranges. This paper defines a fuzzy logic-based methodology to assess the suitability of indoor adaptive temperature ranges proposed by existing regulations of the real thermal sensation of the occupants. For this purpose, the study suggests considering four indicators to evaluate the representativeness of different comfort ranges on the real preferences of the occupants. Based on that, four metrics are proposed for interpreting the needs of the occupants of a building and the changes required to the temperature ranges to satisfy such needs. To validate the proposed methodology, questionnaires on the thermal sensation of the occupants of office buildings considering three case studies (Mediterranean, Europe, ASHRAE) are used, showing the results obtained the representativeness of adaptive comfort ranges for such databases. The feasible introduction of this methodology in a conditioning systems would assist in decision making regarding energy efficiency, thermal comfort and a balance between them. [ABSTRACT FROM AUTHOR]

[Display omitted] • A district load forecasting approach based on the Python framework was proposed. • A heat DR model combining price elasticity and load rebound was developed. • Dynamic pricing strategies were established to optimize customers' load demands. • The proposed IDR model could effectively improve demand-side load flexibility. Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, resulting in a lack of data support for the low-carbon transition. Moreover, demand-side load flexibility has not been fully exploited in the planning stage. In this paper, we developed a two-stage computational approach to optimize district loads. We first established an integrated Python framework, incorporating the TEASER simulation tool and AixLib model library, to efficiently calculate baseline loads through the bottom-up modeling and simulation of district buildings. Then, a price-based integrated demand response strategy was introduced. A mixed-integer nonlinear programming model was formulated to optimize the energy pricing strategy with the objective of minimum load fluctuations. Finally, a case study was employed to illustrate the feasibility of the calculation method, showing a normalized mean bias error of 7.17%. The results further demonstrated that the strategy could reduce the peak electric and heat loads by 3.55% and 9.57%, and increase load rates by 3.85% and 9.48%, respectively. The strategy could assist district energy service providers to optimize equipment capacity configuration and enhance the low-carbon planning potential of energy systems from the demand-side. [ABSTRACT FROM AUTHOR]

[Display omitted] • Infrared technology enables non-invasive temperature measurement. • Existing literature related to personal thermal comfort using infrared technology was reviewed. • Measurement equipment, experimental conditions, subject's information, and analytical methods were analyzed. • Accurate predictions should be translated into control commands for the equipment at different scales. • Diverse application scenarios including car cabin, sleep, and outdoor environments were discussed. Personal thermal comfort studies are crucial for balancing both human comfort and building energy savings. Thus, personal comfort models (PCMs) are expected to play a pivotal role in the development of smart buildings. Infrared technology has received significant attention from researchers because of its non-intrusive nature for collecting personal data. This systematic review examined 36 personal thermal comfort studies that employed infrared technology over the past decade. The review analyzed the (1) infrared devices, (2) experimental designs, (3) subjects, and (4) analysis methods and results. The review found that most studies were experimental and focused on exploring feasibility at a theoretical level, while the number of practical application studies was limited. The accuracy of infrared devices was identified as a primary concern for researchers, and some studies proposed methods to mitigate measurement errors. Most studies included young subjects and examined single clothing insulation and metabolic rates, limiting the generalizability of the corresponding PCMs, which were mainly constructed using machine learning. To facilitate data sharing and comparison of results and to overcome the limitations in terms of applicable populations and application scenarios, the review suggests that standardized data collection and processing should be used in future studies. Furthermore, the paper proposes the integration of PCMs and personal comfort systems, which are two hotspots in personal thermal comfort research, to achieve cooperative applications. Finally, the study discusses the future applications of infrared technology in scenarios such as vehicle cabins, sleep areas, and outdoor environments, considering their unique characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for improving the performance of a direct-expansion solar-assisted heat pump (DX-SAHP) system by integrating a crystallisation controllable phase change material (PCM) and building energy demand shifting strategy. The proposed system aims to reduce buildings' energy consumption while utilizing solar energy efficiently. Sodium acetate trihydrate (SAT) as the crystallisation controllable supercooled PCM has a promising feature of releasing the stored latent heat when it is externally triggered, allowing the user to control the stored heat whenever needed. In the study, the PCM can store the thermal energy generated by the DX-SAHP during peak solar hours in an efficient way. This stored energy can be released when solar energy is unavailable, but the heating is requested. This approach not only reduces the load on the heat pump system but also makes efficient use of the stored thermal energy by crystallisation controllable PCM. A detailed controlling methodology of the system is given, and heating output is controlled considering the level of solar irradiance. The proposed system was modelled and simulated using MATLAB, and the results show that the integration of crystallisation controllable PCM and building demand shifting strategy can significantly reduce the energy consumption of the buildings. On the low solar radiation day, the COP improvement compared to the air source heat pump system was found 9.4%, this improvement value can reach 77% on high solar radiation days. The system can also effectively utilize solar energy and reduce the overall carbon footprint of buildings. [ABSTRACT FROM AUTHOR]

This paper describes the process of validation of the numerical model and its calibration, based on long-term field studies that were carried out at a growing tunnel system for Agaricus bisporus in southern Poland. A verification of uncertainty indices was undertaken, based on which the applicability of the model for further research and analysis was assessed according to commonly used guidelines. WUFI®plus software was applied based on the numerical elementary balance method (EBM). The HVAC system was also simulated in order to reflect actual conditions as accurately as possible. The correctness of the model was assessed by means of uncertainty indicators in the form of the normalised mean deviation error (NMBE) and the Coefficient of Variation of the Root Mean Square Error CV (RMSE). The analysis provided an opportunity to highlight the validity of the calculations, considered reliable under the criteria adopted. It also pointed out some limitations of the model applied, resulting in negative calibration results for a small number of selected measurement monitors. Using the validated calculation model, heat flows from the ground to the building and the other way around were estimated. The specifics of the building contributed to the occurrence of heat gain from the ground being 20 times larger compared to energy losses to the ground. This opens up a potential path for extending the numerical analysis to optimise energy management for the investigated facility and to determine the thermal interaction between buildings in the surveyed area. [ABSTRACT FROM AUTHOR]

Load forecasting necessitates a significant amount of smart meter data. Several elements in this process, including device malfunctions and signal transmission issues, produce missing data gaps. Missing values in the dataset significantly influence the learning ability of machine learning algorithms, and they must be infilled before proceeding with any statistical analysis. This paper investigates the handling of missing values in demand data, and a new approach is developed for improving the performance of demand analytics, such as energy forecasting. The proposed model uses a transformer neural network to impute the missing values at various rates in the demand profile. Our model uses a k-means algorithm to fill in the missing values with proxy values in the dataset. The model is applied to two case-study residential house located in Cornwall and Fintry, United Kingdom. The developed algorithm is assessed for it potential for infilling missing values for three widely understood missing value scenarios: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). The proposed model's imputed outputs are compared to the original dataset to assess model performance. The performance of the framework is compared with a selection of widely used statistical and machine learning models. The proposed transformer model shows significant improvements over the common linear method in all three scenarios (with 30% missing values), with percentage improvements ranging from approximately 49.71% to 57.52% for Cornwall dataset. • A novel imputation framework is proposed for energy demand data. • Testing the model for infilling missing values with MCAR, MAR, MNAR scenarios • Evaluating the model performance in comparison with established benchmark approaches. • Utilising a transformer network to enhance data imputation performance. • Excellent imputation accuracy and model robustness are validated by two datasets. [ABSTRACT FROM AUTHOR]

This paper presents an experimental and analytical study of the effects of the integration of a prototype heat accumulator using water–ice latent heat as an additional hybrid low-temperature heat source for an air source heat pump (HSHP). The study was conducted in two stages. The first stage involved experimental studies on the charging and discharging processes of the prototype accumulator. The second stage was the determination of the possible energy and economic effects of using the proposed hybrid heat source for a single-family building with a thermal output of 7.9 kW. The waste heat from ventilation and gray wastewater was used to charge the studied heat accumulator. A comparative analysis of the HSHP system with two classic solutions—an air source heat pump system (ASHP) and a ground source heat pump (GSHP)—was performed. The results indicated that the HSHP system had 21% and 5% lower electricity demand than ASHP and the GSHP, respectively. An additional benefit of the developed solution was the utilization of 58% of the waste heat of the building, which accounted for 33% heating demand coverage. Economic analysis suggested that the developed HSHP system was a lower cost solution than the GSHP in terms of the investment and operation costs. [ABSTRACT FROM AUTHOR]

• Demand response of heat pump solar water heater was realized by adaptive MPC. • A novel MPC with new boundary settings is proposed to enhance performance. • AMPC realize 20% cost saving and 12% energy saving compared with PID. Full electrification of building energy systems makes that the electricity-driven heat pump and solar heat become the most promising heating sources for hot water production in the future. The heat pump assisted solar water heater will be a good solution to jointly use these two heating sources. The system has large potential in demand response because of large capacity thermal storage tank which requires reasonable optimal control. In this context, this paper presents an adaptive model predictive control (AMPC) to achieve demand response with adaptive boundary and linear time-varying heat pump efficiency. Using adaptive parameters, the controller improves the adaptability and achieves optimal control under various disturbance conditions. The results demonstrated the AMPC realize 20% cost saving and 12% energy saving compared with PID, which are 7% reduction in costs and a 3% decrease in energy consumption compared to conventional MPC methods. Furthermore, a detailed analysis research was conducted on the controller parameters and disturbances, considering equipment parameters, electricity pricing models, weather conditions, and load types. Notably, the MPC exhibited even greater performance improvements in scenarios involving real-time pricing, concentrated loads, and low heat pump capacity. [ABSTRACT FROM AUTHOR]

Reducing CO 2 emissions from the building sector is essential to achieve the goal of mitigating global warming. Soundless chemical demolition (SCD) and induction heating demolition (IHD), as newly proposed demolition technologies, can potentially reduce CO 2 emissions from urban concrete building demolition. To this end, this paper investigated CO 2 emissions caused by blasting demolition and layer-by-layer demolition with SCD and IHD. A framed structure was selected as the case and assumed to be located in 10 cities in China. CO 2 emissions during demolition were divided into three stages for calculation and comparison. CO 2 emissions reducing potential of SCD and IHD were analysed. Results showed that SCD and IHD could reduce CO 2 emissions of blasting demolition, and a longer distance from the city center to the construction waste treatment plant could cause more significant reduction of CO 2 emissions. Meanwhile, CO 2 emissions reducing potential of SCD and IHD were pretty significant. The findings demonstrate that the application and promotion of SCD and IHD can make a significant contribution to the low-carbon and sustainable development of cities. [ABSTRACT FROM AUTHOR]

• Propose a new scale suitable for the photovoltaic construction of urban buildings. • Selection of building complex classification indicators suitable for photovoltaic construction. • The algorithm of K-means + random forest is proposed to complete the classification of building complexes. The large-scale development of urban building photovoltaics (PV) has become an important avenue for cities to achieve building energy conservation, emission reduction, and carbon neutrality. Assessing the solar energy potential of urban buildings plays a crucial role in the installation and overall planning of urban building PV systems. However, due to the vast number and diverse types of urban buildings, individual building calculations can be both labor-intensive and inefficient. Therefore, batch processing of buildings after classification is a feasible approach. Previous assessments of solar energy potential in building clusters mainly focused on typical clusters or standard geometric areas, but all urban building types were covered. Based on this, this paper proposes a method for defining Building Complexes with a Single Land Use Nature (BCSLUN) by combining geographical distribution and urban land use characteristics. Furthermore, population density, building density, compactness, and land use characteristics are selected as features, and a method that combines K-means clustering and random forest classification is proposed to complete the classification of building complexes. Taking the central urban area of Xi'an, China as an example, 5,804 building complexes are classified into four building types. The cross-validation classification accuracy is 0.99, and the Kappa coefficient is 0.94. Based on Rhino modeling, the global solar radiation on the surface of each building complex was calculated. The annual surface global radiation of the four building types is 11,442.73 MWh, 2,734.71 MWh, 13,923.81 MWh, and 67,215 MWh. The violin-box plot shows a significant classification effect, which proves the rationality of the proposed classification method. Finally, a potential map based on solar radiation from urban building surfaces is generated. Building complex classification can quickly and accurately estimate the solar energy potential of urban buildings, providing decision-making support for the large-scale construction of urban building PV. Moreover, this method can be easily applied to other cities. [ABSTRACT FROM AUTHOR]

• Transfer NILM is performed using a mother-son model with an inheritance relationship. • Ratio Squared Error is designed to analyze the experimental results comprehensively. • The performance has been evaluated on five households by five statistical indicators. • In general, our proposed model outperforms three state-of-the-art models. This paper proposes a mother-son model (MSM) to address the challenges of missing data, complex models, and limited transfer capabilities in non-intrusive load monitoring (NILM). The MSM model designs a novel transfer method based on feature inheritance to dynamically decompose multiple appliances (including unknown appliances). The mother model incorporates a six-parallel feature extraction structure to extract comprehensive features, which are then inherited by the son model. The features inherited from the mother model integrate information from multiple dimensions in the spatial domain and focus on local fine features in the temporal domain. The son model offers the capability to adjust the number of appliances and decompose unknown appliances, catering to the requirements of practical applications of NILM. Furthermore, a novel statistical metric, Ratio Squared Error (RSE), is designed to quantify the proportion of squared error in energy consumption. The experiments are carried out on five households and statistically evaluated using five statistical indicators. The results indicate that the son model exhibits superior decomposition performance and greater resilience compared to the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

• Hydronic heating/cooling systems and GCHP performance indicators are briefly described. • Performances of various heating/cooling systems connected to a GCHP are tested in laboratory. • An energy efficiency comparison of radiator heating and radiant heating/cooling is provided. • A comparison of thermal comfort achieved with analysed heating/cooling systems is reported. • Two simulation models of thermal useful energy and COP of these systems are developed in TRNSYS. The method used in this study consists of evaluating different terminal heating/cooling systems in an experimental office at the Polytechnic University of Timișoara, Romania, through experimentation and numerical simulation. This paper is focused on the energy efficiency exploratory research of various low- and very low-temperature hydronic radiant heating/cooling systems (floor, wall, ceiling, combined floor-ceiling) and of the medium-temperature radiator heating system, connected to a vertical ground-coupled heat pump (GCHP) in double U-tube borehole heat exchanger (BHE) configuration. The main performance parameters are obtained for five weeks of operation in heating mode and three weeks in cooling mode, and a comparative analysis of these parameters together with indoor thermal comfort is carried out. The four simple heating systems (radiator, floor, wall, ceiling) have relatively small differences (maximum 7.4 %) in their coefficient of performance (COP) values, but the number of starts/stops in the case of radiator heating is twice that of floor heating, which leads to greater wear and tear on the heat pump equipment. Under the same operating conditions, the CO 2 emission of the radiator heating system are 0.7–16 % higher than those of the heating system with radiant panels (floor, wall, and ceiling). The floor-ceiling combined radiant heating system has the highest COP syst of 5.45 as well as the lowest CO 2 emission value of 2.15 kg. The predicted mean vote (PMV) index (pair 1.1 met-0.29 clo) has values approximately equal to 0 only in the case of the floor-ceiling heating, when the percent of likely dissatisfied with the thermal comfort is 5 %. The three cooling radiant systems show differences in their energy performance, namely the radiant ceiling cooling system has the best COP syst , 39.8 % higher than that of the radiant floor and only 5.9 % higher than that of the radiant wall. The values of the PMV index (pair 1 met-0.9 clo) for the radiant wall cooling system are 31–41 % lower than those of the radiant floor and 10.4–14.2 % lower than those of the radiant ceiling. Finally, numerical simulations of the useful thermal energy and the system COP in heating and cooling modes are performed with TRNSYS software for a duration of 8760 h and then analysed and compared with the experimental tests to validate the simulation models. [ABSTRACT FROM AUTHOR]

[Display omitted] Driven by their low thermal resistance and static transmittance to solar radiation, existing static clear windows are often considered the least-efficient components in the building envelope, driving the heating and cooling demands and compromising the thermal and visual comfort of building occupants. Hence, window retrofit technologies that utilize innovative coatings are critical for enhancing energy efficiency and indoor daylight quality in the existing building stock. In particular, dynamic photochromic window films that reversibly modulate solar gains responding to the abundance of ultraviolet radiation are promising but received little attention. In this paper, five commercial flexible photochromic window films were experimentally characterized, and their energy and daylighting performance were numerically and parametrically evaluated in a representative office with different window configurations and orientations across six representative cities with a diverse range of climates. This is one of the first works dealing with photochromic window films as a retrofit technology. The results show that the films exhibit excellent colour-rendering and cyclic stability with rapid switching/bleaching rates. In addition, annual energy savings in the 1% − 4% range were observed for low window-wall ratios, reaching 10% − 14% in cooling-dominated climates with lower latitudes using PC-1, which resulted in the most significant cooling energy savings. Although the film influenced the quantity of light, resulting in lower daylight autonomy, improvements in the useful daylight illuminance and significant reductions in discomfort glare levels were the major benefits of retrofitting existing double-glazed clear windows with the photochromic film. [ABSTRACT FROM AUTHOR]

Green buildings (GB) have been widely promoted in various nations. However, the post occupancy evaluation suggests many GB cannot well fulfill the expected targets. To overcome the mismatch among GB's multi-objectives, this paper develops an efficient and intelligent hybrid method using BIM-DesignBuilder (BIM-DB), Grey wolf optimization (GWO), random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II) to achieve the optimization of design parameters. The BIM model and DB simulation tool were used to obtain data samples of envelope and air conditioning system design parameters, and their life cycle carbon emission (LCCE), economic cost (EC) and predicted mean vote (PMV). The RF model was used to achieve high precision prediction. The GWO was used in the hyper-parameter optimization. The NSGA-II algorithm was applied to multi-objective optimization to obtain optimal design parameters. A building case shows: (1) The RF model had an excellent prediction performance for LCCE, EC and PMV. (2) BIM-DB can be used to obtain low error and high reliability building simulation data sets. (3) The RF-NSGA-II intelligent algorithm can reduce the LCCE of the building in the entire cycle by 16.6%, reduce the EC per square meter by 2.0%, and greatly improve the thermal comfort by 18.3%, representing good application value. This research provides a way of thinking for the multiobjective optimization of green buildings from the perspective of data mining and guidance for the parameter selection of the envelopes and air conditioning systems of new and existing buildings to more scientifically and effectively design green buildings. [ABSTRACT FROM AUTHOR]

Thermal bridges have a significant impact on the overall energy performance and thermal comfort of buildings. Proper accounting of thermal bridge effects through model coupling leads to more accurate predictions of heating and cooling loads and narrows the performance gap between the design and the actual energy consumption of buildings. In this paper, a dynamic thermal bridge calculation method is introduced in a frequency domain to transform a broad range of multidimensional building envelope assemblies to thermally equivalent RC-Network simple models. RC-Network model with three resistances, four capacitances, and internal and external surface resistances at the ends is found to represent multidimensional thermal bridge assemblies more accurately with minimal computational time. The method is applied for the simulation of diverse two- and three-dimensional thermal bridge details of lightweight and mass constructions exposed to hourly varying sol–air temperature boundary conditions, and the results are compared to reference solutions obtained from transient finite element modeling of the original thermal bridge details. The highest RMSE of the proposed RC-Network model for the lightweight and mass construction details are 0.12 W/m2 and 0.37 W/m2, respectively. The low RMSE values reaffirm the good performance of the method and its appropriateness for the calculation of hourly heat flux through multidimensional thermal bridge details. [ABSTRACT FROM AUTHOR]

[Display omitted] • Retrofit strategies like automated shading boost energy efficiency and comfort. • Heat transfer shifts from solar gain in summer to conductive loss in winter. • Cold climate shade optimization needs season-specific controls, insulative materials. • Factors like window orientation, solar intensity, climate, outdoor temperature impact system. • Cold-optimized automated shading shows a 5–10 year payback period. Enhancing urban sustainability requires innovative strategies to improve energy performance while maintaining occupant comfort in new and existing buildings. Buildings located in cold climate zones with high glazing-to-wall ratios often face unwanted solar gain and heat loss that, in turn, negatively impact building energy performance. Though automated shading systems effectively reduce excessive indoor solar gain in warm climates, a research gap exists regarding their ability to enhance building energy performance in heating-dominant climates. This paper investigates automated shading implementation for cold climate zones and develops a novel cold climate optimized sensorless control strategy for interior roller shades. A field study was conducted to assess the impact of roller shade operation on indoor thermal conditions. Results from the field study showed that the prototype roller shades operating on the developed control strategy reduced daytime indoor peak temperatures during the summer season to 26.4 °C ± 0.5 °C from 30.0 °C ± 1.9 °C and 28.5 °C ± 1.2 °C for the unshaded and shaded static operation baseline scenarios, respectively. During winter season tests, shade deployment reduced the overnight indoor cooling rate from 0.60 °C/hr ± 0.30 °C/hr and 0.42 °C/hr ± 0.18 °C/hr. A mathematical model was also developed to estimate shading system energy performance based on user-defined building specifications and weather-related variables. It was found that automated shading systems are suitable for use as a green retrofit strategy for cold climate zone buildings. The technology exhibited a payback period range of approximately 5–15 years, depending on factors such as glazing type, glazing orientation, solar exposure, and local climate conditions. Findings from both the simulation and prototype field study support the use of cold climate-optimized automated shading systems to improve overall building energy performance - reducing both building heating and cooling-related energy consumption. [ABSTRACT FROM AUTHOR]

• Resistance-Capacitance models are combined with finite difference / volume methods. • The model adapts with a daily parameter update using only 2–4 weeks training data. • The grey-box model adjusts automatically between heating and cooling operation. • A simple façade model with glazing and shading is validated against a detailed one. • Model performance is demonstrated with 1 year of data from a real office building. Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a grey-box model suitable for MPC is not straightforward, especially in buildings combining, not only ventilation and usual internal loads, but also Thermally Activated Building Structures (TABS) and large glass façades with external shading. To address these complexities, this paper presents a reduced order grey-box approach, considering all these elements. Various single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for modelling the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. Additionally, the influence of technical parameters on the model's performance is investigated. The best model variant, with an enhanced glass façade model, achieves an accuracy of 0.25 °C of Mean Absolute Error over a year of simulation, on the 24 h zone temperature forecast compared to the measurement. This model has a small number of parameters (8), which are estimated with the least square non-linear method. The stability of the parameter values is analysed. The parameter identification requires only a small historical dataset of 1–2 weeks for startup and 2–4 weeks for training. This provides an adaptive model, in the sense that it is updated regularly (every day or week) based on recent measurement data. This data-driven evolving model is suitable across a wide range of applications involving data-driven Model Predictive Control (MPC) for buildings. [ABSTRACT FROM AUTHOR]

At present, China's public building energy efficiency improvement projects lack vitality in the market due to the weak awareness of energy efficiency among the participating subjects. Therefore, improving the knowledge and awareness of all participating parties has become the key to effectively promoting energy efficiency improvement in Chinese public buildings. This paper researches the knowledge demand of key subjects for energy efficiency improvement in public buildings(EEIPB), establishes a knowledge demand indicator system for EEIPB, and proposes targeted knowledge improvement methods. Firstly, the knowledge demand of key subjects for EEIPB is identified by using grounded theory, and the knowledge demand indicator system for EEIPB is established. Secondly, the knowledge demand categories are determined with the help of the improved Kano's model, and the priority of the knowledge demand elements of each subject is derived through Better-Worse index analysis. Finally, suggestions on the learning paths for Better-Worse index analysis are put forward. It is found that the demand for various subjects for knowledge on EEIPB is mainly focused on five aspects: incentive policies, regulations, standard specifications, market, and technology. In terms of demand, the types of knowledge demand elements for EEIPB of each subject are mainly One-dimensional Attributes and Mixed Attributes among which incentive policies and market are the focus of attention of each subject. In addition, there are both commonalities and differences in the knowledge demands of each subject. This research will help government authorities formulate effective promotion measures for the demand for energy-efficiency improvement knowledge in public buildings and raise the level of energy-efficiency knowledge awareness of all subjects in the public building industry. [ABSTRACT FROM AUTHOR]

• An HVAC demand response model is developed and validated with electricity meter data. • Demand response functions for offices, schools and data centres are determined. • The impact of parameters on demand response is assessed by parametric simulations. • Of the three typologies, school buildings show the highest relative DR potential. • Aggregate DR potential is estimated to be between 551 and 647 MW. Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake in commercial buildings is yet to occur, in part due to the difficulty of characterising the resource. This paper describes a bottom-up physics-based approach to characterise the DR potential of three types of commercial buildings (schools, offices, and data centres) under a global set-point temperature offset strategy. Representative models are calibrated with energy meter data and parametric analysis is used to assess sensitivity to different building, operating and system parameters. Parametric equations are provided for relative DR potentials as functions of temperature and time of day. School buildings were found to have the highest relative DR potential (∼40–45%) for ambient temperatures over 30 °C, followed by data centres (∼20–30%) and offices (∼20%). Location has the strongest relative influence for school and office buildings and equipment energy intensity for data centres. The Australia-wide combined DR potential for school, office and data centres is estimated to be between 551 and 647 MW. Office buildings have the highest aggregate potential at between 1.5 and 1.7 times that of school buildings and between 9 and 11 times that of data centres. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

[Display omitted] • A numerical model is proposed for seasonal thermal energy storage (STES) couple with solar collector. • The model is featured as relatively high computational speed and accuracy. • An analysis on technical planning and operational design for the STES is offered. • The key parameters for system design and control are investigated with instructive results. Seasonal thermal energy storage (STES) systems are a key component in expanding the share of renewables in energy programs because they provide schedulability and flexibility. However, such a large-scale system requires careful planning to avoid high investment costs. Therefore, numerical models are becoming increasingly important as an alternative. This paper develops a numerical model of STES coupled with solar collector. The model was verified based on the experimental data of the Huangdicheng Project in China. The results show that the relative error in the charging mode and discharging mode is only 1.57 % and 0.46 %, respectively. Then, the effects of different charging and discharging mode on the heat storage efficiency of the tank and the efficiency of solar collector systems in STES were studied. The study found that in the initial charging stage, the water temperature rise curve caused by different flow rates is very different. In the design of the collector-storage area ratio, the relatively economical collector-storage ratio of this model is around 3768 L/m2. The selection of different proportions of discharging energy in the discharge stage has a great impact on the heat storage efficiency of the system in the next year. Moreover, the influence of different depth-diameter ratios of the tank on the system heat storage efficiency is discussed in detail, which has important guiding significance for model application and system analysis. This paper provides some references for the scale design and operation optimization of cylindrical STES. [ABSTRACT FROM AUTHOR]

[Display omitted] • The paper reviews the state of the art of thermal 3D point clouds (TPCs), their uses and current limitations. • The paper presents the current methods dealing with TPCs: calibration and correction, data acquisition, data processing and applications. • The paper shows the principal limitations, gaps, and future developments of TPCs in Architecture, Engineering and Construction. New methodologies that explore and identify energy problems of buildings can be defined by using three-dimensional thermal models over time. The acquisition of thermal point clouds is the primary link in a chain that ends with a semantic Building Information Model (BIM) that contains energetic parameters. This literature review attempts to outline the current state of the art as regards the most important stages in this path by discussing the topics of thermal point cloud acquisition systems, 3D thermal semantic models, exportation to thermal models and the current applications of thermal point clouds in Architecture, Engineering and Construction. Special mention is made of the limitations and gaps in each stage. As a summary, it can be stated that only simple thermal BIM models of single constructive elements have been defined to date, and that a long path must be followed in order to automatically obtain a thermal BIM model of a multi-story building. [ABSTRACT FROM AUTHOR]

• Propose a seq2point non-intrusive conforming decomposition model with a deep feature-guided attention mechanism. • Variable sliding windows are used to obtain strongly correlated power series according to the operating cycles of different appliances. • Using word embedding technique to transform regression problem into multi-classification problem to improve the accuracy of power decomposition. • Snapshot ensemble techniques are used to further improve the generalization ability and accuracy of the model. Non-intrusive load monitoring (NILM), as an important part of intelligent electricity consumption, improves the cognitive level of the load by analyzing the bus power in a specific range to obtain refined electricity consumption data at appliance level in that range, such as the number of loads, load categories, load operating status and corresponding energy consumption. For a long time, the research on NILM algorithm has been quite in depth, especially with the deep learning method being used in NILM, breakthrough progress has been made. In this paper, an integrated policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism is constructed based on sequence-to-point deep neural networks. Firstly, according to the operating cycle characteristics of different appliances, the data are segmented with different sliding windows and the integerized power values are mapped to high-dimensional feature vectors by word embedding, which transforms the value regression problem of decomposed power into a multiclassification problem to find the probability of decomposed power values at each discrete integer power value. Then the encoder composed of deep residual network is used for multi-layer feature extraction, which is used to obtain richer feature information, and the extracted feature quantities will be sent to the decoder through two paths respectively, one is mapped to the power feature space through word embedding as the input to the decoder; the other is sent to the decoder based entirely on the attention mechanism through convolution as the checked vector K and the content vector V. The decoder obtains the information with the highest relevance to the current moment by assigning weights to the before and after information, and thus decodes the final load decomposition result. Finally, this paper introduces a snapshot technique to obtain an inexpensive multi-model integration strategy, which improves the generalization ability of the model and the accuracy of the load decomposition. The effectiveness of the proposed method is verified by experiments on the REDD dataset and UK-DALE dataset. [ABSTRACT FROM AUTHOR]

Summer peak electric demand of commercial buildings and districts is typically driven by the cooling load. This paper presents a novel approach to holistically optimize both the air- and water-side of the HVAC system in existing commercial districts. Therefore, this paper is different from the existing literature in its focus on integrated optimization of both air- and water-side of the HVAC system in a district scale as opposed to only airside or waterside. Methodologies proposed in this paper for integrated optimization of air- and water-side are brand-agnostic and their capabilities are demonstrated using a chilled water plant at Colorado School of Mines' campus. Based on the results for the test district, Mines' integrated air- and water-side optimizations of the HVAC system reduces its peak electric demand by 15% and 13%, respectively, for August 19 and 20, 2019. The air- and water-side optimization of the HVAC system resulted in greater savings than that achieved with only the waterside demand optimization method discussed in authors' previous paper focused on existing commercial districts. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper introduces the frost phenomenon on the cold plane and evaporator surface. • This paper gives a potential research and development interests in frost field. • Hydrophobicity is a preferable frosting retardation method, which are summarised in detail. Frosting begins when the surface temperature falls below both the water freezing temperature and the moist air dew point temperature. Frost formation is a widespread phenomenon, but in essence, it is a nonlinear and complex physical change involving heat and mass transfer. People's understanding of frost is still mainly in the initial experimental observation and empirical calculation stage. Therefore, this paper presents a panoramic view of frost research status in concise language, including the frost generation and propagation mechanism, the measurement and calculation of frost physical properties, the arrangement of frost experiment, and the simulation of frost. Then we summarize the methods of surface frosting retardation, among which surface hydrophobicity treatment is considered the most effective. The mechanism of hydrophobicity on frosting retardation and the preparation of the hydrophobic surface are summarised detailedly. Several defrosting methods are introduced later. Finally, the potential directions for further research are listed. [ABSTRACT FROM AUTHOR]