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Highlights • Definition of passive envelope indexes and caracterization of libraries in La Plata. • Environmental monitoring: achievement of preventive conservation indexes. • Correlation between passive envelope indexes and preventive conservation indexes. • Determination of the most influential passive envelope indexes for the achievement of preventive conservation indexes. Abstract The common approach for libraries to preserve their paper-based collections from irreversible deterioration is by controlling quantitative parameters, such as temperature and relative humidity. Variations of these parameters for preventive conservation can be expressed by the "performance index" (PI) and the "amplitude index" (AI). Building envelope is the first filter of the external climate, buffering the external weather fluctuations and maintaining the building's indoor climate stable. Its shape, window areas and envelope materials are the most influential parameters. As they capture the relationship of a few geometric and physical variables with the building's performance, the envelope characteristics can be expressed as certain passive envelope indexes. This paper studies the relationship of certain passive envelope indexes and the preventive conservation indexes for books for several libraries and archives located in La Plata, Argentina. The results show the impact of every passive envelope index upon the preventive conservation indexes. The compactness, heaviness, adjacency to spaces and adjacency to ground have a positive linear correlation meanwhile transparency and global transmittance are parameters with a negative correlation. In general, the most influential parameter is the relative area adjacent to other spaces, followed by global transmittance, transparency, heaviness, relative area adjacent to ground and compactness. [ABSTRACT FROM AUTHOR]

Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

Abstract: Buildings consume a vast amount of energy during the life cycle stages of construction, use and demolition. Total life cycle energy use in a building consists of two components: embodied and operational energy. Embodied energy is expended in the processes of building material production, on-site delivery, construction, maintenance, renovation and final demolition. Operational energy is consumed in operating the buildings. Studies have revealed the growing significance of embodied energy inherent in buildings and have demonstrated its relationship to carbon emissions. Current interpretations of embodied energy are quite unclear and vary greatly, and embodied energy databases suffer from the problems of variation and incomparability. Parameters differ and cause significant variation in reported embodied energy figures. Studies either followed the international Life Cycle Assessment (LCA) standards or did not mention compliance with any standard. Literature states that the current LCA standards fail to provide complete guidance and do not address some important issues. It also recommends developing a set of standards to streamline the embodied energy calculation process. This paper discusses parameters causing problems in embodied energy data and identifies unresolved issues in current LCA standards. We also recommend an approach to derive guidelines that could be developed into a globally accepted protocol. [Copyright &y& Elsevier]

Abstract: Construction of sustainable, energy efficient buildings, infrastructure and transportation systems is a worthwhile and necessary goal to address critical worldwide environmental and energy concerns. However, as societies move toward this goal, it is the position of the International Forum of Fire Research Directors that the safety of the public and the protection of property against losses due to fire should not, and need not, be compromised in its pursuit. Fire safety consideration should form an unrenouncable part of any new product or technology developed to promote sustainable construction, and acceptable solutions to acute fire safety concerns must not pose a threat to the long term health of workers, fire fighters or the general public. This paper concentrates primarily on fire safety performance of new products, materials and technologies associated with the move toward sustainable communities, while recognizing that there are also issues confronting the fire safe design of buildings and infrastructure with features such as natural daylighting, natural ventilation, “smart facade” technologies, intelligent building controls, and water reduction strategies. [Copyright &y& Elsevier]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

This paper reviews applied single and hybrid solar energy-saving techniques with emphasis on solar chimney, Trombe wall, and photovoltaics for building energy consumption and thermal comfort. Solar energy techniques can be broadly classified into passive and active. Passive strategies refer to collecting, storing, and distributing solar energy without relying on electrical or mechanical components. In contrast, active techniques rely on mechanical and electrical components. The discussed passive devices include solar chimney and Trombe wall. Different design configurations have been reported to influence various buildings' thermal and electrical performances. On the other hand, active solar strategies in buildings including photovoltaic technologies, are discussed. Moreover, applied the hybrid system of these techniques in building is discussed. The review paper critically analyzes these systems' energy and thermal performances to demonstrate their significance in building applications. The significant findings of these studies demonstrate the capability of these systems to provide thermal comfort and considerable energy savings. The review studies indicated that passive architecture could improve thermal (as high as 20%) and visual comfort and reduce electricity consumption by 46%. Besides, several highlights on BIPV and BIPV/T systems are discussed, showcasing their status, recent research, development, and applications toward achieving building energy efficiency. Throughout the review, it is seen that solar technologies applied in buildings have undergone a remarkable revolution, demonstrated significant energy-saving potentials and opening the road for application in net-zero energy buildings. Further research is required on some of the technologies to assess the techno-economic feasibility of these systems in the building sector. [ABSTRACT FROM AUTHOR]

Efficient thermal energy distribution and rational energy management are effective solutions to address the significant and fast-ascending energy consumption in building heating, ventilation, and air conditioning (HVAC). Phase change material (PCM) slurries with excellent thermal energy transportation and storage characteristics have been recognised as promising secondary refrigerants to simultaneously facilitate efficient heat/cold transport and effective thermal energy storage in air conditioning systems. Among various PCM slurries, salt hydrate PCM slurries have attracted increasing attention in recent years due to their many inherent advantages. This paper presented the preparation, characterisation, and property modification of a CaCl 2 ·6H 2 O slurry based on a methodology proposed for optimal salt hydrate PCM slurry development. Theoretical modelling was first carried out to assess the availability of salt hydrate PCM slurries based on the phase diagram and solid fraction, followed by a series of property measurements to reveal the mechanisms and address the problems behind the phase transition, crystal particle morphology, stability and rheologic characteristics, using nucleating agents, surfactants and stabilisers. It was found that the salt hydrate PCM slurry developed provides a unit volume enthalpy twice that of water over the phase change temperature range of 5°C, together with acceptable fluidity. The results also showed that the PCM particle size can be well controlled, and the slurry can be stabilised, with the assistance of surfactants and stabilisers. Compared to other types of PCM slurries, the proposed salt hydrate PCM slurry has the advantages of being cost-effectiveness, easy preparation, adjustable phase change temperature, etc. , which is promising to facilitate energy saving and rationalisation in building HVAC. [ABSTRACT FROM AUTHOR]

[Display omitted] Two significant manifestations of the passive effect of building photovoltaic (PV) systems include the enhancement of the thermal performance of the envelope and the reduction of building energy consumption through the installation of PV systems on the building envelope's surface. This paper presents a review of the passive impact of PV roof systems, PV wall systems, and PV window systems, as well as the effects of the external environment, building ontology conditions, and PV system configuration on the passive effects. The study concludes that in regions with a hot climate, the shading effect of PV roof and PV wall systems during the summer can reduce the building's cooling load by 20% to 70%. In frigid climate regions, heating energy consumption during the winter can be reduced by 10% to 20% through the PV wall system's ability to recycle hot air in the cavity. Indoor thermal comfort can be improved while reducing lighting energy consumption by 3% to 14% with PV windows. A comprehensive analysis of the existing literature on the influencing factors reveals that the influencing factors of the passive effect have complex uncertainties and non-linear coupling characteristics under various conditions. To provide a theoretical foundation for the scientific popularization of photovoltaic systems, this paper identifies current research problems and prospective research directions for building photovoltaic systems based on the current status of research in the literature. [ABSTRACT FROM AUTHOR]

• Publicly available data was collected to construct reference buildings. • Random personnel behavior was modeled using time use survey data and Markov chains. • Building scenes in unbuilt areas were created using scenario generation algorithms. • A 28.3 % reduction in prediction error was achieved compared to traditional methods. • A more precise spatial distribution of energy consumption is illustrated. Urban Building Energy Model (UBEM) is a widely used method for load forecasting in urban energy planning. It typically depends on reference buildings to simplify particular scenarios and uses energy calculation software for load forecasting. However, significant challenges exist when using this approach in unbuilt areas, because there is no established distribution of buildings or a widely recognized database of reference buildings. To overcome this technical barrier, this paper introduces a new workflow - Scenario Urban Building Energy Model (S-UBEM). This approach utilizes publicly available databases to establish a reference building database and a stochastic human behavior database. By utilizing these databases and planning documents, the methodology can construct building energy use scenarios for the undeveloped area, ultimately achieving load forecasting. In this paper, the proposed method is validated by transforming the built-up area into an unbuilt state. The results demonstrate that S-UBEM more accurately represents the spatial and temporal distribution of energy consumption. This is evidenced by a 28.3 % reduction in the annual energy prediction error compared to the conventional method, as well as by visualizing the distribution of energy consumption at the building level. [Display omitted] [ABSTRACT FROM AUTHOR]

High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications. The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency. A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings. [ABSTRACT FROM AUTHOR]

• Energy building simulations are little based on their effect on the microclimate, most applications deal with energy consumption or the neglect of the Solar Reflective Index (SRI) rule outside. • The paper framework introduces a comprehensive simulation workflow of the built environment with the assistance of Ladybug & Honeybee and Butterfly plugins across grasshopper interface to model a shared relationship between urban microclimate, energy building use and thermal comfort outdoors. • The result describes the effect of SRI on the outdoor thermal comfort for the pedestrians in existing canyon when defining the finishing materials for the urban zone rather than the rural zone for both districts at the same time the transpiration phenomenon is exempted along the simulation with no effect of vegetation. • "Strong heat stress" is the main output conclusion for both districts as mentioned by UTCI stress category and the difference between the urban and rural zones is about 1 °C due to the materials absorption and emissions. Energy building simulations are little based on their effect on the microclimate, most of software applications deal with energy consumption for appliances usage electricity for indoor spaces and neglecting the impact of the Solar Reflective Index (SRI) phenomenon for outdoor open spaces. The paper framework introduces a comprehensive simulation workflow of the built environment with the assistance of Ladybug & Honeybee and Butterfly plugins across grasshopper interface to model a shared relationship between urban microclimate, building energy usage, and thermal comfort outdoors. Two districts have been investigated in Egypt, both have different Azimuth and Latitude angle degrees to measure the impact of solar radiation on pedestrians and their outdoor thermal comfort using Mean Radiant Temperature and Universal Thermal Climate Index (UTCI). This workflow provides considerable versatility compared with other current methods and allows parametric study of multiple environment strategies both indoors and outdoors. The result describes the effect of SRI are too noticeable on the outdoor thermal comfort for the pedestrians in existing canyon, especially after defining the material specs and properties for each building. As considerably, the transpiration phenomenon is exempted along the simulation with no effect of vegetation and their humidity affect. "Strong heat stress" is the main output conclusion for both districts as categorized by UTCI stress level and the difference between the condense urban and rural areas from measuring the Mean Radiant Temperature is about 3 °C for Shaykh Zayed district and 1 °C for the Fifth Settlement district and this is due to the materials absorption and emissions for the long and short waves, respectively. [ABSTRACT FROM AUTHOR]

• Pilot study of solar/hybrid/storage system for vertical farming under tropical climate condition with a case study at Malaysia. • Energy utilization and management via optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. • Comprehensive analysis and assessments including technological, economic and environmental aspects of the proposed work. • The proposed work is evaluated under different design configuration (grid connected, stand alone, hybrid, storage etc) considering various optimization and design requirements and real-life constraints. • Comparative studies with hypothetical model and verification with additional energy modeling tool. Human population is projected to reach 9.7 billion in 2050 and strike 11.2 billion by 2100. Residence shift from countryside to cities will occur and the human population resides in the cities is envisaged to rise from 55% to 68%. Vertical farming was proposed to address food insecurity caused by overcrowded population and farmland shortages. The compound annual growth rate (CAGR) of vertical farming in the Asia Pacific is forecasted to grow at 22.1% with a market size reaching USD 2,101.0 million by 2024. Malaysian government encourages the implementation of vertical farming as the production of major agricultural commodities has contracted by 3.4% from 2015 to 2017. However, high energy consumption is one of the shortcomings of a vertical farm as the estimated annual energy consumption for each square meter of growing area is 3500 kW h. Thus, this paper proposes an optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. This research examines the load demand in the vertical farming systems and develops solar/hybrid/storage for vertical farming system with energy yield, performance ratio, economics and environmental assessments. Preliminary resources assessment by analyzing the solar radiation of the sites was carried out at stage 1. At stage 2, load consumption was calculated, and specifications of the system were justified. Stage 3 included the development of solar PV system for the vertical farms, the economic evaluation in the context of net present cost (NPC), levelized cost of energy (LCOE) and investigation of the environmental impact. Grid-connected and standalone solar PV systems were developed for comparative studies. The estimated daily energy consumption for vertical farms in Selangor and Kuching was 430.116 kW h and 1002.024 kW h. Energy generated by grid-connected solar PV systems supported 11.6% and 8.35% of the load consumption in Selangor and Sarawak. The performance ratios of grid-connected solar PV systems in both selected sites were 82.22% and 82.56%. The finding proved that proposed work has significantly reduced the dependency of the utility grid. Additionally, the LCOE with solar PV integration vertical farming system appeared to be lower than the LCOE of the grid and lower CO 2 emission. This paper serves as a reference guide to the vertical farming stakeholders, national policy-decision makers and non-profit organizations who planned for similar initiatives especially those within the tropical climate regions. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

Because of their low computational costs, surrogate models (SMs), also known as meta-models, have attracted attention as simplified approximations of detailed simulations. Besides conventional statistical approaches, machine-learning techniques, such as neural networks (NNs), have been used to develop surrogate models. However, surrogate models based on NNs are currently not developed in a consistent manner. The development process of the models is not adequately described in most studies. There may be some doubt regarding the abilities of such models due to a lack of documented validation. In order to address these issues, this paper presents a protocol for the systematic development of NN-based surrogate models and how the procedure should be reported and justified. The protocol covers the model development procedure sample generation, data processing, SM training and validation, how to report the implementation, and how to justify the modeling choices. The protocol is used to critically review the quality of NN-based SMs in the prediction of building energy consumption. Sixty-eight papers are reviewed, and details of the developed surrogate models are summarized. The reported developing procedures were evaluated using the criteria proposed in the protocol. The results show that the selection of the number of neurons is the best-implemented step with a justification, followed by the determination of model architecture, mostly justified in a discussion way. While greater focus should be given to sample dataset generation, especially input variables selection, considering independence check and clear report of model validation on training and test data. Also, data preprocessing is strongly recommended. [Display omitted] • A protocol for developing and evaluating surrogate models. • A systematic procedure for developing neural-network-based surrogate models. • Overall quality assessment of developed surrogate models in building energy. • Typical configurations used in building energy neural network-based surrogate models. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

Accurate prediction of building energy consumption is crucial to the rational scheme of building energy. Combining the entropy-weighted K-means (EWKM) with the random forest (RF) method, a feature selection (EWKM-RF) method is proposed in this paper. Based on the proposed EWKM-RF method, the classification and feature selection of the energy consumption influencing factors can be achieved exclusively. Meanwhile, based on the EWKM-RF method and the bi-directional long-short-term memory neural network (BiLSTM) optimized by the sparrow search algorithm (SSA), an RF-SSA-BiLSTM prediction model for building energy consumption is established in this paper. As the weight, learning rate, and hidden layer node parameters of the BiLSTM neural network are optimized with the SSA, the constraints for manually adjusting parameters are avoided in the proposed prediction model. To examine the accuracy of the proposed model, energy consumption data of a civil public building in Dalian city are collected and tested. Results show the prediction error of RF-SSA-BiLSTM after feature selection is reduced by 24.55 % in high and low energy consumption months. Compared with RF-BiLSTM, RF-PSO-BiLSTM, and RF-CNN-BiLSTM, the RF-SSA-BiLSTM has strong robustness. The average MAE, RMSE and MAPE values of energy consumption prediction in the four seasons are 1.30, 1.63 and 0.02. [Display omitted] • A new prediction method is developed for building energy consumption. • The integration of SSA and BiLSTM neural network is analyzed. • Combinations of EWKM with RF algorithm to reduce influencing factors. • Deep learning is used to extract features of building energy consumption data. • Comparison of the proposed prediction models with different optimization models. [ABSTRACT FROM AUTHOR]

• Carbon capturing technology is widely used in fossil fuel power generation sector to reduce the greenhouse gas emissions. • This paper presents a feasibility study of implementing carbon capturing technology on building heating systems. • It evaluates life cycle environmental impacts and costs of implementing carbon capturing on building heating systems and introduces a decision-making framework. • The paper shows carbon capturing technologies have the potential to be implemented in building heating systems compared to other greenhouse gas emissions mitigations technologies. Buildings are responsible for 40 % of the global energy consumption, thus leading to significant climate change and other environmental impacts. Adopting carbon capturing, storage, and utilization technologies (CCSU) can be a solution for reducing greenhouse gas (GHG) emissions from natural gas building heating systems. However, the current knowledge base lacks definitive knowledge on the possibilities and impacts of building level CCSU. The goal of this study is to investigate the feasibility of implementing CCSU technologies in natural gas building heating systems. A comparative performance assessment framework was developed to evaluate the environmental and economic performance of building-level CCSU technologies over their life cycle. These performances were then compared against alternative GHG emissions mitigation methods used for building heating systems. The results obtained by a case study indicated that adopting CCSU technologies in residential buildings is not economically and environmentally competitive against electricity-based heating systems when the regional electricity is generated using renewable energy sources and nuclear energy. However, there is a potential for the building-level CCSU in regions that depend on fossil fuels to generate electricity. The research outcomes will further develop and improve building-level carbon capturing technologies and will provide confidence to stakeholders to invest. [Display omitted] [ABSTRACT FROM AUTHOR]

Various studies have assessed the energy performance alterations affected by the novel technology of Building-Integrated Photovoltaic in Double-Skin Facades (BIPV-DSF), while lighting performance tied to the BIPV-DSF has not received much attention. This paper provides numerical modelling to assess the effect of BIPV-DSF on both indoor visual condition and energy consumption for an office module under a typical climate in the United Kingdom. The proposed study was focused on the comparisons between a reference case (a DSF office module with both layers using clear double glazing) and a design case of the same office module with BIPV-DSF using semi-transparent Amorphous Silicon PV glazing. Results show a significant drop in maximum daylight illuminance of 73% by configuring the BIPV-DSF with reference to the regular DSF. It was also reported the resultant average and minimum daylight factors (0.65% and 0.00%) were not able to meet indoor visual comfort requirements for office environments. Furthermore, it was found that the use of BIPV-DSF resulted in a net increase of 8% in building energy consumption over the reference DSF. Therefore, it is concluded that in the present context the BIPV-DSF is not viable for a commercial installation under the UK's climate conditions. [ABSTRACT FROM AUTHOR]

Reducing building energy consumption and increasing the use of renewable energy are essential to energy conservation. Photovoltaic thermoelectric heat pumps have been considered as a sustainable measure to supply detached houses with hot water, especially those that even do not have access to the power grid. This paper investigates a stand-alone photovoltaic/thermal integrated thermoelectric water heating system (PV/T-TEWH). A dynamic model of PV/T-TEWH is developed and validated. The performance of the system in Lhasa, Beijing, Changsha and Guangzhou is simulated and analyzed respectively, and the economics of the system are compared. Results show that PV/T-TEWH can rely on the electric energy it generates without additional power supply when the current is less than or equal to 1.4 A in Lhasa and 1.0 A in Beijing, Changsha and Guangzhou. The annual total heat gain of the PV/T-TEWH system increases as the operating current increases, but the COP of the system falls with the rise of operating current. When the system is operating at the currents of 0.6 A, 1.0 A, 1.4 A, and 1.8 A, the payback period shortens with the increase of current. Lhasa has the shortest payback period among four cities and it is only 2.2 years when the working current is 1.8 A. The PV/T-TEWH system provides a new method for building hot water supply, especially for detached houses in remote areas. • This paper proposes and investigates a photovoltaic/thermal integrated thermoelectric water heating system (PV/T-TEWH). • A dynamic model suitable for simulating the PV/T-TEWH system is developed and validated. • The annual performance of the system in Lhasa, Beijing, Changsha and Guangzhou is simulated and analyzed. • The economics of the system in different cities are compared. [ABSTRACT FROM AUTHOR]

In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings. [ABSTRACT FROM AUTHOR]

Prefabricated housing estates represent an important urban, architectural and historical phenomenon in the Czech Republic. Most people prefer to live in prefabricated buildings because of their central location, good transport services and a lot of amenities in their vicinity. Owners or tenants of flats prefer prefabricated buildings after revitalization, as they are more affordable than new developments and at the same time do not have such high energy consumption costs as prefabricated buildings without revitalization. The paper aims to focus on the energy savings of a specific prefabricated building. The heating consumption and the resulting monetary savings are addressed. The aim of the research is the performance of economic and life cycle analyses of the building. Firstly, the original condition of the prefabricated building and the extent of the revitalization carried out in 2001 were specified. Secondly, an energy assessment of the apartment building was carried out, where the heating consumption before and after the revitalization of the apartment building was determined and the resulting heat savings for each year of the assessment were determined in the appropriate units (GJ, kWh/(m2/yr)). Economic indicators and life cycle costs were determined based on the modelling of the heat price development and possible energy savings. Currently, there has been a huge increase in energy prices. The reason for the price increase is the current situation in the wholesale market, where the prices have been reaching extreme amounts for several months and their further development is difficult to predict. The energy crisis has deepened as a result of the geopolitical situation. Customers are expected to experience a further increase in the electricity and gas prices since the prices can increase by up to tens of per cent. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings, which in large part provides comfortable conditions for occupants, in many countries is significant and increasing. Many factors need to be considered to establish comfortable conditions, and the presence of each is important for a building. One of these is ventilation which is achievable in two chief ways: mechanical ventilation and natural ventilation. Solar chimney systems enhance natural ventilation through buildings via buoyancy forces. In the present paper, an improved design for a solar chimney for a two-story building is investigated computationally. The dimensions of each floor are 8.5 m × 5 m × 3.65 m, for a floor volume of 155 m3. Initially, a two-dimensional computational fluid dynamics approach is utilized to specify the influence of changing diagonal angle on the ventilation rates of the floors and the relationship between geometric parameters of the system and ventilation rate. In the second phase of this paper, three-dimensional computational fluid dynamics approaches are used to computationally assess the improved design for the sloped solar chimney in order to determine the most effective geometry for inducing a high number of air changes per hour for the specific case. Also, the numerical approaches allow integration of the new modified geometry for a sloped solar chimney as well as assessment of the ventilation rate of the system and comparisons of the results with those for conventional geometries. The results demonstrate that changing the inclination angle of the titled surface is significant whenever the length of this part is sufficiently long. Also, it is shown that the improved geometry takes on a funnel form in order to eliminate the effects caused by dissipative flow phenomena as well as turbulence and vortices. Furthermore, it is observed that the new geometry enhances the natural ventilation rate of the building by 24% compared to conventional designs. • Enhanced solar chimneys induce higher natural ventilation compared to straight ones. • Overall air flow induced by new solar chimney configuration enhanced by up to 24%. • Ventilation for first and second floors improved by up to 24% and 12.5%, respectively. • Air flows delivered from first and second floors have various behaviors in tilted part. • First floor ventilation positively affected by increasing tilted part slope and length. [ABSTRACT FROM AUTHOR]

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems. [ABSTRACT FROM AUTHOR]

Buildings' energy consumption and greenhouse gas emissions are a major global concern. Healthcare buildings, being crucial to society, pose particular challenges due to their round-the-clock operation and stringent hygiene standards. This paper comprehensively reviews existing literature to promote energy-efficient and comfortable healthcare buildings. The research explores both passive measures, such as orientation, materials, and daylight, and active measures, including Heating, Ventilation, and Air Conditioning systems, energy management, and renewable resources. The paper emphasizes the critical role of user behavior in conserving energy and outlines how factors like building size, operation hours, and climate can impact resource consumption. It highlights the importance of solar power as a prominent renewable energy source. It offers design and retrofitting options to enhance healthcare buildings and addresses the lack of research on small-scale healthcare buildings. The paper emphasizes maintaining a balance between user comfort and energy reduction, involving diverse stakeholders, and exploring benchmarks, automated shading, geothermal sources, local materials, and their impact on carbon emissions. This review aims to contribute to environmentally responsible and socially resilient healthcare infrastructure and provide insights for future challenges in creating energy-efficient healthcare buildings. • Literature review of "sustainable", "green", and "smart" healthcare building strategy. • A comprehensive examination of passive and active measures for energy-efficiency. • Findings highlight user behavior's critical role in energy conservation. [ABSTRACT FROM AUTHOR]

Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate. • Feature engineering method using deep learning to mine features. • Analyzed the impact of features on predictive models using cooperative game theory. • Analyzed the impact of dataset size and number of features on model predictions. • Developed an integrated model for building energy consumption prediction. • The main factors affecting energy consumption in educational buildings are discussed. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

China is facing the challenge of high building energy consumption, especially for rural housing, because of the backward construction technology. This issue could be solved by net-zero energy rural housing. Therefore, the passive energy-saving procedures and energy-efficient building service systems of rural housing are needed to be studied apart from renewable energy adoption. Older people with unique thermal acceptability and adaptability become the primary residents in Chinese rural buildings, which calls for the redefinition of the indoor environment. This paper firstly conducted an overall questionnaire about the indoor environment sensation and expectations of the rural elderly in Sichuan province. Field surveys and measurements were carried out in a typical self-built rural house to investigate the thermal comfort of the local order people and the indoor thermal environment variations. Furthermore, the fuzzy analytic hierarchy process was used to propose a three-layer assessment model for the rural elderly living environment to find retrofit strategies. Results showed that: (1) the thermal environment of rural buildings could not meet the thermal comfort range of older people in summer. (2) The adaptive coefficient (λ) in the adaptive Predicted Mean Vote model was 0.065 for the rural elderly, which was lower than the value of 0.21 in the Chinese standard, indicating their poor thermal adaptability. (3) The summer living environment evaluation was "A little bad", and the most effective optimization method was to decrease the indoor temperature. (4) Based on the thermal comfort of older people, architectural and construction optimizations were proposed, which could achieve an energy-saving of 52.15 %, contributing to net-zero energy rural housing. • Environment improvement and energy saving of rural housing were studied. • The age-based metabolic rate of the elderly was proposed in aPMV. • The adaptive coefficient for the rural elderly was 0.065. • The evaluation model of rural living environment satisfaction was built by FAHP. • Passive retrofit procedures were proposed to achieve NZERH. [ABSTRACT FROM AUTHOR]

Modern buildings are highly glazed and necessitate an enormous amount of energy to maintain thermal and visual comfort. To reduce the energy requirements of such buildings, conventional glazings should be replaced by energy-efficient glazing systems. In this paper, the cost-effective insulating material is used in the glazing application. This glazing system is made by filling the crystal mud transparent slime (TS) in the air gap of the conventional double-glazing unit. The solar-optical and colour rendering properties, thermal properties, solar heat gains, yearly net operating costs and cost savings, repayment period, and CO 2 emission mitigation of transparent slime glazings with varied gaps, including 4 mm, 8 mm, and 12 mm, were evaluated. These evaluations were made for the Sholapur (hot and dry) and Lucknow (composite) climatic zones of India. This study revealed the potential of TS glazings in reducing the heat gains, net operating costs and CO 2 emissions compared to conventional double-glazing units (ADGUs) with 4 mm, 8 mm and 12 mm air gaps. In comparison to conventional air-filled double glazing with a 12 mm gap (ADGU-12), transparent slime glazing with a 12 mm gap (TS-12) has demonstrated the greatest savings in heat gain of 21.3 % and yearly net operating cost of 21.32 %. TS-12 has recorded the highest cost savings of $132.62 and $119.85 for the Sholapur and Lucknow climatic zones respectively. TS-12 obtained the highest CO 2 emission mitigation of 2.107 (tCO 2 /year) for Sholapur climatic conditions. All the TS glazings have shown a very less repayment period ranging from 4.97 to 6.16 years. Daylighting analysis for a dining hall building model with TS glazings revealed adequate natural illumination inside the building. All the TS glazings have shown good colour quality and uniform daylighting. This work gives a novel idea for reducing the energy consumption of buildings. • A novel crystal mud transparent slime glazing system for buildings • Transparent slime glazing system as a replacement to conventional double glazing systems • Solar optical properties of transparent slime glazing systems • Transparent slime glazing system for reduced annual net cooling and heating costs • Transparent slime glazing system for enhanced daylight factors and useful daylight illuminance [ABSTRACT FROM AUTHOR]

At the conceptual design stage, the building geometry design can predict the building load demand and renewable energy application potential to some extent. Geometry design interventions are critical to efficiently achieving the goal of zero-energy buildings. In the existing studies, the typicality and representativeness of the object geometry in the practice project are not high, and the influence of different geometry indices on energy savings and photovoltaic (PV) utilization potential under the same type of plane shape is rarely considered. In addition, the surrounding building shading situations considered are single. There is still significant room for improvement in the typical geometries selection and optimization condition settings. This paper presents a study of three typical park office building geometries in hot summer and cold winter regions. An optimization method based on the genetic algorithm and energy consumption simulation is proposed that takes into account both building energy savings and PV utilization potential improvements. The geometry-related indices of each typical geometry are then optimized under different PV layout scenarios and surrounding shading situations, and suitable geometry design strategies for typical office buildings are proposed under each optimization scenario. Geometry optimization can significantly reduce building energy consumption (the building energy-saving rate is 1.3–10.7 %) and increase PV generation (enhancement of 13.37–66.67 % for PV generation potential), thus lowering the net energy consumption of buildings. The findings of this study can be used to guide the design of park office building geometries in hot summer and cold winter regions with the goal of zero energy, as well as to promote the practice of zero-energy office buildings at the regional and national levels. • Three typical geometries are identified through research and analysis of 199 buildings. • A building geometry optimization method based on the genetic algorithm is proposed. • Building energy savings and photovoltaic utilization potential are considered. • Different photovoltaic layout scenarios and shading situations are carried out. • Optimization can significantly lower the net energy consumption of buildings. [ABSTRACT FROM AUTHOR]

For green building design, this paper proposes a multi-objective optimization (MOO) framework to properly adjust design parameters using a deep reinforcement learning (DRL) approach. There are three key parts incorporated, including an assessment index system construction, a deep neural network (DNN) model training, and the automatic generation of an optimal solution set based on the deep deterministic policy gradient (DDPG) model. The efficacy of the DRL methodology is evaluated in a three-story educational facility situated in Shanghai. Results indicate that the DDPG model outperforms traditional genetic algorithms in simultaneously optimizing three objectives represented by building energy consumption, carbon emission, and indoor thermal discomfort, yielding a 13.19% improvement in building performance. Particular emphasis should be placed on the DDPG agent to intelligently and automatically learn better policies through iterative interactions with the environment created by Building Information Modeling (BIM) and DNN, thereby offering data-driven decision support for green building design endeavors. • A DRL approach is proposed to offer decision support for BIM-based green building design. • DRL incorporates DNN and DDPG to optimize building performance as a MOO problem. • DRL can return accurate predictions on building performance and optimal strategies for building design. • DDPG shows superior optimization performance over traditional MOO algorithms with robustness, stability, and generalization. [ABSTRACT FROM AUTHOR]

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under "representative concentration pathway" (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European "energy performance of buildings" directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that annual thermal energy demand in Finland in 2080 can decrease up to 74.6 kWh/m 2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in South Finland (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better. [Display omitted] • Thermal energy demand of detached houses in Finland is studied under RCP climate scenarios. • Thermal energy demand is expected to decrease in Finland in long-term. • RCP scenario affects when the highest average annual thermal energy change occurs. • Cooling can become the dominant thermal peak demand source for passive buildings. • Renovating building to newer standards remains beneficial, though less with time. [ABSTRACT FROM AUTHOR]

The construction industry's excessive energy usage necessitates practical solutions to address this pressing issue. Windows are crucial architectural elements as they allow the majority of natural light to enter a structure. The poorly designed window and related components cause the space to overheat, consuming more energy. However, it negatively impacts the building's thermal and visual comfort. The paper introduces a novel approach for optimizing control parameters for egg shadings through multi-objective methods. The recommended approach significantly reduces the building's energy consumption and enhances its thermal and visual comfort. EnergyPlus is a software utilized for conducting energy-related simulations. The JEPLUS software has therefore considered seven design variables, such as differences in the vertical and horizontal angles of the shade, length and horizontal dimensions, depth and vertical dimensions, and window positioning. These year-round simulations are performed in four different orientations for four different Iranian towns with various climates. The data is improved by using the program JEPLUS + EA. To find the best locations on the Pareto front, the data is optimized using the NSGA-II approach. The optimization results indicate that a lower angle improves visual comfort but takes more electricity for lighting. The findings of multi-objective optimization of controlled blind characteristics improved thermal and visual comfort with ranges of 5–45% and 55–100%, respectively, and lowered overall building energy consumption by 1–19% yearly, depending on the building's geographic orientation. [ABSTRACT FROM AUTHOR]

• A solar-thermal integrated system based on the full spectrum solar energy utilization is proposed. • The three-dimensional model combined optical simulation to flow field simulation is constructed. • A photothermal utilization experimental platform was built. • The average optical transmission efficiency reaches 45.9 %. • The average heat absorption efficiency is 40.56 %. • The effects of solar irradiance and flow rate on the system's performance are discussed. Solar lighting technology has significant potential to reduce building energy consumption. However, current solar lighting systems utilize only visible light, neglecting the potential of infrared radiation energy. This study proposes a novel photothermal system utilizing the full spectrum of solar energy. This paper proposes a novel photothermal system utilizing the full spectrum of solar energy. The collector cavity and frequency-division film are designed to recover invisible light and convert it into usable heat energy. The innovative combination of film and prism further mitigates damage to the optical fiber caused by light concentration. During the experiment, the system's average optical transmission efficiency was 45.9 %, and the average illumination output was 13.4 klux. The average heat absorption efficiency was 40.56 %, converting recovered invisible light into approximately 1.6 MJ of heat energy. Three-dimensional modeling of the system was performed using optical and flow field simulation software. Simulation results were compared with experimental data to verify the accuracy of the model. Then, based on the above experiments and modeling, the influence of various factors on the system performance is analyzed. Radiation intensity and flow rate significantly influence the system's outlet temperature. The average change rates of the outlet temperature, influenced by these two factors, are 19.4 % and 3.44 %, respectively. The outlet temperature is highest when the air flow rate is 0.4 m/s. The flow rate minimally affects the optical transmission efficiency, with an average change rate in outlet temperature of only 2.71 %. [ABSTRACT FROM AUTHOR]

Existing data-driven models of building energy consumption have incorporated solar irradiance as a covariate without accounting for key physical constraints, leaving room for improvement in model performance and interpretability. In this paper, we propose the Time-Of-Week, Solar and Temperature (TOWST) model for quantifying building energy consumption. This new model incorporates solar gain into the well-known Time-Of-Week and Temperature (TOWT) model by accounting for important physics constraints, including, building orientation and the opposite impact of solar gain on heating and cooling. An iterative algorithm is developed to cluster the energy demand into heating and cooling categories, identify building orientation, and estimate the unknown model parameters. The model is applied to datasets generated from a building simulation according to international standard ISO 52016-1:2017, for a range of buildings types and locations. Results show a 30%–72% reduction in mean squared error relative to the TOWT model, across a range of different climatic conditions, demonstrating the significance of accounting for solar gain in a physically meaningful way. • Data-driven building energy model incorporating the physics of solar gain. • Identifies building orientation to capture the irradiance received by a building. • Infers heating and cooling hidden states to model the physical dependence. • Significant improvement in model fit across range of climatic conditions. [ABSTRACT FROM AUTHOR]

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs' power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level. • Proposing a new hybrid architecture for predicting the large-scale EVs' power demand. • Constructing a new transfer learning strategy to improve the prediction accuracy. • The proposed hybrid model significantly improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Trombe wall technology, a passive solar design strategy, has garnered significant interest for its potential to reduce building energy consumption. This paper presents a comprehensive review of the evaluation and optimization trends for Trombe walls. The historical evolution and recent advancements in Trombe wall systems are critically analyzed. Furthermore, key optimizing parameters are discussed in detail, including material selection, wall thickness, insulation characteristics, window specifications, vent dimensions, and intelligent control strategies. Common experimental methodologies and simulation approaches employed for Trombe wall optimization are summarized. Finally, current research limitations are identified and future research directions are proposed to fill existing knowledge gaps and facilitate the widespread adoption of optimized Trombe wall systems for improved energy efficiency in buildings. • One figure shows the timeline of Trombe wall development. • Subdivided statistical analysis of Trombe wall research trends in the past five years. • Rich and detailed original Trombe wall diagram. [ABSTRACT FROM AUTHOR]

The development and utilization of solar energy, meteorological forecasting, and agricultural production all require accurate predictions of radiance. However, due to the influence of meteorological conditions and the failure of observation instruments and design defects, the data collection of all-sky radiance distribution is often missing or inaccurate. In this paper, a new modified method of all-sky radiance distribution (CASR) is proposed based on the Spatial distribution of daylight model recommended by Commission Internationale de l'Eclairage (CIE model). Firstly, a functional relationship between sky luminance and radiance was established based on measured data. Secondly, the abnormal values were divided into four categories according to the number of anomalous regions of radiance and the frequency of abnormal values in each sky region is determined, and the reasons were analyzed according to different altitude angles. Thirdly, the radiance and sky luminance anormal values were screened to determine their sky type by comparing them to a dataset of 15 standard sky types. The abnormal values were then corrected using the CIE luminance model with the relationship between radiance and sky luminance. Finally, the CASR model was validated against existing models. The results show that the CASR model accuracy was better than the existing model, which can accurately correct the sky luminance and radiance abnormal values and accurately characterize the distribution of radiance of the whole sky. It provides a theoretical basis for radiance prediction, natural lighting analysis and building energy consumption analysis. • Linear and polynomial functional relationships between radiance and sky luminance were established. • Classified and summarized the anomaly data according to their causes and the number of radiance anomaly regions. • A new modified method of all-sky radiance distribution based on the principle of photothermal integration was proposed. [ABSTRACT FROM AUTHOR]

The operational state of energy-efficient ultra-low energy consumption buildings is very important to achieve energy savings and emission reductions, which are currently necessary considerations for the development of the building industry. To date, studies on the implementation of ultra-low energy consumption have been based mainly on the analysis of the effects of certain technologies, and few studies have examined the regional implementation paths at different energy consumption levels. Based on an analysis of actual data obtained from energy consumption monitoring, verification of energy-saving renovations and green building evaluations in Chongqing, this paper suggests setting the target value for ultra-low energy consumption buildings in hot summers and cold winters areas at 29.04 kWh/(m2.a), proposes a technology system that includes the practical application of data for seven active energy-efficient technologies, and establishes an implementation path for the integrated application of active and renewable energy technologies. Then, a set of strategic models for the construction of implementation paths for ultra-low energy consumption buildings that can be promoted in different climatic areas and building types is proposed. The recommendations based on this model are of practical value for guiding policy makers in promoting the development of ultra-low energy consumption buildings. • Determined ULEB target value based on actual Chongqing energy consumption data • Proposed energy-efficient technology system based on effect and cost analysis • Proposed replicable strategic models for implementation path of ULEBs construction [ABSTRACT FROM AUTHOR]

In this paper, a semi-nested building coupled with an earth-to-air heat exchanger is investigated to maintain indoor thermal comfort and reduce building energy consumption. Firstly, the computer models on the semi-nested building coupled with earth-to-air heat exchanger were developed under the TRNSYS environment. Secondly, the simulation results from the computer models were validated with experimental data from open literature. Finally, iterative Targuchi method was employed to find the optimal design values for the envelope properties and earth-to-air heat exchanger configurations. The optimal values of the design parameters were found to be A1 (window-to-wall ratio of 10%), B3 (triple-layer Low-E glazing window), C3(insulation thickness of 200 mm for external wall),D3 (concrete slab thickness of 250 mm for external wall), E3(absorption of solar radiation for external walls of 0.75), F3(absorption of solar radiation for the roof of 0.75),G5 (earth tube inner diameter and air gap of 300 mm and 400 mm, respectively), H2 (earth tube length of 50 m), and I3 (duct air velocity of 1.5 m/s). [ABSTRACT FROM AUTHOR]

As an important component of building energy consumption, wall heat loss needs to be studied urgently. Wall implanted with heat pipes (WIHP) is one of the latest research hotspots in this field. Firstly, the internal temperature distribution characteristics of WIHP are studied by numerical simulation in this paper. Secondly, the thermal conversion efficiency is proposed to evaluate the utilization of solar energy by WIHP. Finally, the energy saving potential of WIHP in heating season is analyzed. The results show that: (1) WIHP has a significant anisotropic heat transfer characteristic compared with the conventional wall; the average time lag of the inside surface is reduced by 2.3 h, and the average decrement factor is increased by 0.008. (2) The time lag difference between the upper and lower parts of WIHP is 6 h, and the time lag of the WIHP structural layer is increased by 1 h compared with the conventional wall. (3) The heat transfer of heat pipe increases with the increase of solar radiation, but the thermal conversion efficiency decreases. (4) WIHP can decrease the heat loss of the wall by 56% on average, and the cumulative heat loss is reduced by 5.29 kWh/m2. • WIHP has a significant anisotropic heat transfer characteristic. • The thermal conversion efficiency as a new evaluation indicator is proposed. • The thermal conversion efficiency of WIHP decreases with the increase of solar radiation. • The time lag of WIHP inside surface is reduced by 2.3 h. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the energy efficiency of educational buildings with the case study of buildings in City University of Hong Kong by constructing an energy demand stochastic frontier model. The model is estimated by using the university statistical data from 2011 to 2015. For the consistent frequency of data among the variables, we have adopted the quadratic-match sum method to convert annual university report data into a monthly dataset. Our result shows the average energy efficiency is 0.87, implying that 13% of total energy consumption can be saved. We then calculate how much of the energy saving potential has been achieved by constructing the performance score, which increases from 0.08 to 0.17. It implies that the campus performs more efficiently in saving energy over time. We further develop econometric decomposition analysis based on the energy demand frontier model to identify the factors affecting energy consumption. It suggests that research activities account for a large share of overall energy consumption. Analysis on energy end-use shows university should improve efficiency in lab instrument as it is least efficient among the four usages. We expect this paper can provide the fundamental and methodological guideline for university-scale energy efficiency estimation. • Estimating energy efficiency of educational buildings needs to consider the composition of research related factors. • Energy efficiency of campus building presents increasing tendency with seasonal cycle over time. • Research related variables affect significantly on energy efficiency of educational buildings. • Energy use in lab instrument needs to be effectively managed to save energy. [ABSTRACT FROM AUTHOR]

• A novel agent-based model (ABM) was developed to explore occupant behaviors in the context of built environment. • Relevant data was collected using customized sensor nodes and paper-based survey in a test bed building. • A validation study was conducted to test the occupant behavior model with the methods of visualization and calculation of quantification metrics for performance evaluation. • The proposed research framework has the potential to improve both building energy efficiency and occupant comfort during the life cycle of a building. Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs. [ABSTRACT FROM AUTHOR]

World Cities contribute to a massive amount of carbon emissions. About 80% of the global greenhouse gasses are emitted from these cities. The sustainable practices should be adopted in built environment for these cities. As an example, a residential neighbourhoods in arid climate has been discussed as a case study in practice in this paper. It has been developed to implement the sustainable strategies and concepts. It demonstrates a mixed-use community which integrates the sustainability dimensions to improve the quality toward sustainable living in a small community (about 4000 persons), allocated in Dubai land area. This paper aims to highlight the sustainable practices in the arid zones and to give an overview about the performance of the clean energy and renewable in these areas. The studied development has been selected to evaluate such techniques and strategies in the gulf region (Dubai). Various environmental categories such as; on energy, water, mobility, energy consumption of the buildings and infrastructure, urban green spaces, solid waste management and urban farming, have been appraised in this study. It has been noticed that the urban design proposed reduces its potable water consumption by 40% and for energy demands by 57% compared to conventional households. The solar production can cover around 38% of the energy consumption on average. Many challenges face the sustainability movement in the region, especially, the affordability and feasibility in such harsh climate zones areas. This manuscript opens new perspectives about the prioritization that should be considered for adopting the sustainable strategies in arid climate. Image 1 [ABSTRACT FROM AUTHOR]

Abstract Transparent Insulation (TI) systems are regarded as one of the most promising technologies for providing thermal insulation along with transmission of solar energy. TI systems have a wide range of applications in energy conservation and harnessing solar energy. This paper provides an overview of TI systems and TI materials (TIM), by characterising TI systems on the basis of geometry, material used and general heat losses in these geometrical layouts. Evolution of TI applications is presented with the introduction of new materials. Two new types of TIM (polypropylene and cellulose acetate) are reported in this paper in contrast to previous reviews on TIM. A systematic survey of past literature was carried out to present latest research works and development on TI geometrical layouts and applications. An explicit study of TIM and TI system manufacturers has been carried out to show existing trends in TI applications and geometry. In the last decade, aerogels are most frequently used as TIM for providing insulation and daylighting in buildings. It is concluded that in existing TIM technologies, TI systems incorporating aerogel have the lowest thermal transmittance (U-value) and better solar transmittance (g-value) at lower thickness compared with TI systems incorporating other types of TIM. This paper highlights the availability of TIM geometry with desirable applications and TI systems for energy conservation. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive review of the significant studies exploited Artificial Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of the relevant studies to the scope of the research, a three-decade time span of the publishing date of the existing studies was taken into account. The review focuses on the studies utilized ANN to analyze the energy-related issues associated with buildings in major areas, including modeling of water heating and cooling systems, heating and cooling loads prediction, modeling heating ventilation air conditioning systems, indoor air temperature prediction, and building energy consumption prediction. Moreover, the findings of the abundant reviewed studies along with the potential future research to be carried out are discussed elaborately. Regarding the comprehensive review conducted, it is found out that the majority of studies focused on building energy consumption and indoor air temperature prediction. Additionally, it is observed that there has been a growing interest in the application of newly-developed ANNs to BEA areas, such as general regression neural network and recurrent neural network, due to their abilities in improving the modeling and prediction of buildings energy analysis. It is believed that this thorough review paper is useful for the researchers and scientific engineers working on the application of AI-based techniques to the building-energy-related areas to find out the relevant references and current state of the field. [ABSTRACT FROM AUTHOR]

Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso—group lasso and adaptive lasso—outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies are discussed in the context of the low-carbon transition. • Reviews literature on modeling energy consumption and determinants of residential electricity consumption. • Reviews regularization methods in regression analysis to improve prediction and model interpretability. • Assembles dataset of 58 predictive factors and annual electricity usage for one thousand households California. • Applies five regularization techniques to these data to compare model prediction and variable selection. • Highlights influence of occupant socio-demographics, physical dwelling characteristics, and occupant behaviors. [ABSTRACT FROM AUTHOR]

Abstract The assessment of building energy performance through dynamic simulations has been increasing significantly in recent years since it represents a key strategy for the correct design of highly efficient buildings. Results of dynamic energy simulations are affected by many uncertainties, and its reliability depends on the accuracy of the input variables. One of the most influential variables is the climate surrounding the building, a reason why the use of accurate weather data files is essential, but experimental datasets are not always available. In this context, this paper analyses numerical weather datasets obtained from different regional climate models by comparing them with real data; in addition, it evaluates their impact on the energy performance of a historical building in Asunción through dynamic simulations. The database of five different weather data sources is compared with observed meteorological data in order to assess their accuracy through statistical analyses. Moreover, some methodologies to estimate diffused and direct components of the global solar radiation are evaluated, with the objective of solving the problem of missing direct and diffused solar data components from the meteorological codes. Subsequently, weather data files are generated to quantify the influence of measured/simulated meteorological data on the evaluation of building energy performance. The results obtained in this paper show that the simulated meteorological data agree very well with real observations for the year under study. Also, the simulations of the building energy performance delivered similar values to those obtained using the real weather dataset. Therefore, the regional climate models can represent a reliable tool for building energy performance assessment, and mainly for the calibration of building energy models when measured weather data is not available. [ABSTRACT FROM AUTHOR]

Abstract Over the last decade, building engineers have begun implementing sensible-latent de-coupled air-conditioning systems in non-residential buildings in hot-humid climates. Typically, a de-coupled system consists of a dedicated outdoor air sub-system for ventilation and high-temperature radiant cooling sub-system for space cooling. This approach has lowered cooling energy demands and fan energy consumption in buildings. However, the complete energy saving potential of de-coupled systems, specifically high-temperature space cooling sub-systems, can only be realized by using low-lift chillers that supply chilled water which matches the high-temperature radiant cooling application. This paper discusses the results of a retro-installation of a prototype modular low-lift chiller to provide high-temperature chilled water (at 17 °C) to the radiant cooling units. The paper presents temperature and cooling load profiles, control methodology and energy efficiency improvements of the low-lift cooling system. The paper also further discusses the influence of various parameters on the performance of the chiller. Despite having a cooling capacity of only 20 kW, the modular low-lift chiller is consistently able to achieve a Coefficient of Performance (COP) of 8. By operating at a low-lift (20 K) compared to a high-lift (29 K), the sensible cooling electrical consumption (at the chiller) is reduced by 23% despite being compared to a high-lift low-temperature chiller with 100 x higher cooling capacity. When normalized for cooling capacities, this reduction is between 29–30%. The project is the first known installation of low-lift cooling for building comfort control in hot-humid climates, and first reported publication of low-lift cooling in a real-life scenario - i.e., non-laboratory conditions or modelling exercise. [ABSTRACT FROM AUTHOR]