Building Energy Consumption Research Articles

Phase change hydrogel formed by osmotic pressure for thermal energy storage

The present study investigates the potential of latent heat storage using phase change materials (PCMs) to reduce building energy consumption, addressing the common challenges of PCM leakage and compatibility with construction materials. We introduce a novel phase change hydrogel, created by encapsulating polyethylene glycol (PEG) within a superabsorbent polymer (SAP) network through osmotic pressure. The structural and thermal characteristics of the PEG/SAP composite were analyzed using differential scanning calorimetry (DSC), thermal gravimetric analysis (TG), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and polarizing optical microscopy (POM). Results demonstrated that PEG effectively filled the SAP network, forming a stable composite structure, with PEG as the core and SAP as the supporting matrix. The composite achieved a remarkable PEG mass fraction of 80 %, yielding a melting enthalpy of 129.7 J/g and a peak melting temperature of 51.89 °C. Additionally, PEG/SAP exhibited robust thermal stability up to 300 °C. Notably, when incorporated into cement, surface temperatures of phase change cement decreased by 12 °C compared to conventional Portland cement under constant heating. This approach highlights high latent heat capacity, low production costs, and straightforward fabrication, positioning PEG/SAP as a promising solution for large-scale applications in building energy conservation.

Exploring the natural ventilation potential of urban climate for high-rise buildings across different climatic zones

The natural cooling capacity of the environment can effectively reduce the building energy consumption by natural ventilation (NV). For architects, developing NV strategies requires a comprehensive understanding of the distribution characteristics of urban meteorology. This study investigates the vertical characteristics of urban local-scale meteorological parameters across climatic zones. It aims to assess the climatic potential for NV of outdoor air in high-rise buildings. Firstly, using Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) data and sounding data, the vertical distribution of meteorological parameters are explored in typical urban areas across different climatic zones. Then, based on an adaptive thermal comfort model, the climatic potential is assessed for NV in high-rise buildings via the indicator of annual natural ventilation hour (NVH). Finally, the characteristics of urban meteorology is further is analyzed in terms of its impact on the natural ventilation potential (NVP) in each climatic zone. Results indicate that air temperature decreases with height from 0 to 500 m, with more pronounced temperature gradients in compact building areas. Near the ground level, Kunming (Temperate Zone) has the most NVH, reaching 4,840 h annually. However, when building height exceeds 100 m, Guangzhou (Hot Summer and Warm Winter, HSWW) becomes more suitable for NV. In cold regions, low temperatures limit NVP during winter, while high humidity constrains it in Temperate and HSWW zones. Overall, the annual NVP in high-rise building areas exceeds that of low-rise building areas. These distribution characteristics of NVP are anticipated to promote the utilization of natural resources for sustainable urban development.

BIM and orthogonal test methods to optimize the energy consumption of green buildings

The construction industry’s rapid growth significantly impacts energy consumption and environmental health. It is crucial to develop optimization strategies to enhance green building energy efficiency and encompass comprehensive analysis methods. This study aims to introduce and validate a novel framework for optimizing energy efficiency design in green buildings by integrating Building Information Modeling (BIM) technology, Life Cycle Cost (LCC) analysis, and orthogonal testing methods, focusing on enhancing energy efficiency and reducing life cycle costs. The optimization parameters for the building envelope are identified by analyzing energy consumption components and key green building factors. The orthogonal testing method was applied to streamline design options. Building Energy Consumption Simulation (BECS) software and LCC analysis tools were employed to calculate each optimized option’s total annual energy consumption and the current life cycle costs. Using the efficiency coefficient method, each optimization scheme’s energy consumption and economic indicators were thoroughly analyzed. The framework’s validity and applicability were confirmed through an empirical analysis of a campus green building case in Fujian Province, demonstrating that the optimized framework could reduce energy consumption by 4.85 kWh/m2 per year and lower costs by 38.89 Yuan/m2 compared to the reference building. The case study highlights the framework’s significant benefits in enhancing environmental performance and economic gains. The results provide critical parameter selection and offer scientific and technological support for the design of building energy efficiency, promoting optimization techniques and sustainable development within the construction industry.

Personalized federated learning for buildings energy consumption forecasting

Buildings' energy consumption forecasting is critical for energy saving and building maintenance. However, most studies only focus on centralized learning of one dataset, which ignores the data privacy and data shortage issue. Meanwhile, the difference in energy data distributions from many buildings causes difficulties in training a good machine learning model. Although these two challenges of data privacy and data heterogeneity could be resolved through personalized federated learning algorithms to some degree, there is still a lack of investigation into applying these algorithms to building energy data analytics. Besides using existing personalized federated learning algorithms, we design a new deep learning model through a mixture of experts to support personalization for heterogeneous data distribution. This new design is the first trial to tackle the data heterogeneity through ensemble architecture in federated load forecasting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model with different training algorithms. The results show that our proposed method outperforms other state-of-the-art models in energy forecasting accuracies by 10% to 40% across the buildings' energy data from university campuses.

Urban functional area building carbon emission reduction driven by three-dimensional compact urban forms’ optimization

Previous optimization approaches with regard to the overall urban form can no longer meet the demand for accurately implementing building energy consumption carbon emission reduction at small spatial scales. This study constructed building energy consumption-oriented urban functional area division method using points of point-of-interest (POI), three-dimensional building and roadway data with urban functional areas as units. The method linked POI to buildings, using building volume as an auxiliary parameter to determine the functional attributes of blocks while taking into account the building operational energy consumption. The residential functional areas, commercial functional areas and industrial functional areas, which account for more than 90% of the total building operation energy consumption and the top three in terms of share, are selected as the analysis objects. Geographic detector was applied to analyze the mechanism of carbon metabolism in the compact spatial patterns of these three functional areas. The case study in Xiamen, China reveals that (1) Three-dimensional building compactness is an important factor influencing building energy consumption(normalized difference vegetation index q-value is 0.227); (2) the difference in energy consumption between different compactness classes in residential and commercial functional areas is significant, while not in industrial functional areas; (3) according to the three-dimensional building compactness optimization path of this study, the building operation energy consumption in central Xiamen City could be optimized to reduce by 7% in 2015. Based on the self-created building energy consumption-oriented functional area division method, it is concluded at the functional areas scale that different types of urban functional zones have different optimization methods for three-dimensional building compactness. Such optimization can save construction resources and achieve double carbon more effectively.

Microclimate Effect on Cooling Energy for Buildings in Hot, Humid Climates: A Comparative Analysis of Shaded and Unshaded Environments

This paper explores the influence of microclimates on changes in air temperature and the often-overlooked aspect of their effect on energy savings across varying microclimatic conditions. The study compares the cooling energy requirements of two identical single-story buildings in distinct microclimates: one characterized by concrete ground devoid of shade and the other featuring soil ground with tree shade. Climatic environmental data were collected over 15 days in the concrete-exposed field and shaded area beneath the trees to conduct the investigation. These datasets were input into EnergyPlus 9.6 to model the energy demands and consumption of buildings subject to the specified climatic conditions. The validation of the simulated model against actual energy demand data from a classroom building demonstrated agreement. The findings reveal notable differences in air temperature, with the shaded area experiencing temperatures 0.8°C to 8.0°C lower than the concrete-exposed monitoring location. The building in the tree-shaded microclimate exhibited a lower peak cooling load than its concrete-exposed counterpart, resulting in a 35% reduction in the electrical energy requirements for the air-conditioning system. The study recommends implementing 0.08-m polyurethane insulation for the building walls and roof to equalize the energy demand and consumption of the concrete-exposed building with that of its shaded counterpart. Furthermore, building design in shaded areas can maximize the window glass area while consuming less energy than buildings on concrete-exposed grounds. The study advocates leveraging the microclimate associated with surrounding buildings in the design process to enhance the overall energy savings.

A novel dual-wheel air-conditioning system operating strategy in different seasons

The application of solid desiccant air-conditioning systems can significantly reduce building energy consumption. Previous studies have focused on innovating the integration of solid desiccant air-conditioning systems (DACS) to improve dehumidification performance in summer. In this paper, a novel DACS is proposed, which can humidify the supply air in winter based on satisfying the dehumidification demands in summer. It can satisfy the year-round humidity requirements of domestic housing with significant savings in humidification costs. Furthermore, the operating strategy of the novel DACS is proposed, particularly for winter humidification conditions. It is adaptable as well as cost-effective compared to traditional operating strategies. The dehumidification/humidification capacity of the core component, polymer desiccant wheel, and the variation of temperature rise/temperature drop are investigated under different variations of parameters, including air temperature, air humidity, air volume flow rate, and wheel speed. The results show that with the increase of supply air mass flow from 0.6 kg/s to 1.9 kg/s, the power consumption of the system only increases by 9.5 kW. In winter, when the supply air temperature decreases from 8 °C to 1 °C, the power consumption increases by 7.2 kW, but in summer, when the temperature of the supply air increases by 7 °C, the power consumption of the system decreases by 7.0 kW. The power consumption of the system varies greatly when the humidity of the supply air is less than 17 g/kg, conversely, the power consumption of the system showed a negative trend. The analysis demonstrates the effective performance of the proposed system under varying climatic conditions.

Investigation of Building Automation Systems in Terms of Lighting Efficiency

Considering that energy consumption is constantly increasing along with a gradual decrease in energy resource reserves, energy is the most current and pending issue in today's world. Several academic studies have been conducted on the efficient use of energy resources. For this purpose, the idea of "smart building" has been proposed to use energy more efficiently in buildings. With building automation systems, which are indispensable elements of these smart buildings, the energy consumption of buildings can be greatly reduced. In this study, the lighting efficiency of a building automation system built at a training point in the Marmara University Mehmet Genç campus, Vocational School of Technical Sciences, was examined. The Sauter Vision Center software program was used at the observation center of the system. The results were analyzed in terms of efficiency by comparing the energy values in certain time periods obtained from the program with the energy values calculated in full use. The analysis yielded a lighting efficiency value of 54%, which was found to exceed the values reported in the literature.

Fabrication and building energy-saving performance evaluation of polyethylene glycol/polymethyl methacrylate/expanded graphite thermal enhanced shape-stable phase change material

Integrating Phase Change Material (PCM) with building envelope is a promising way to reduce building energy consumption. However, leakage and insufficient PCM loading rate for existing encapsulation strategies limit its further application. In this work, a safer and easier way to fabricate Shape-Stable Phase Change Material (SSPCM) is proposed, whose products combine the outstanding leakage-proof property and high PCM loading rate. With the method of in-situ polymerization, not only the Polyethylene Glycol (PEG) is packaged by Polymethyl Methacrylate (PMMA), but the Expanded Graphite (EG) also has a chance to be added as the property regulator. The prepared SSPCMs are proved to be shape-stable over melting temperature with a leakage rate under 1 %. The characterization test reveals that with EG mass fraction increase from 0 to 2.5 %, the thermal conductivity augments from 0.23 to 1.73 W/(m·K) while the phase change latent enthalpy changes from 85.11 kJ/kg to 116.10 kJ/kg. Notably, the EG is more than a thermal conductivity enhancer, it also plays an important role in the modification of comprehensive properties. To optimize the building energy-saving performance of SSPCMs, the factors affecting building cooling electricity consumption like deployment position and thermal properties are evaluated in detail. The simulation results show that the optimum strategy is employing SSPCM with 2.0 wt% EG in the middle of the envelope, the cooling electricity consumption can be reduced by 8.64 % compared with the benchmark. The physical mechanism of energy-saving is also discussed from multiple angles. From a statistical view, sensitivity and uncertainty analysis of the simulation results is completed, to identify the influence of input parameters and validate the reliability of the simulation results. Finally, an economic analysis is carried out, proving the economic feasibility for further application. This research provides valuable insights for SSPCM’s application and design of energy-efficient buildings.

A study on the enhancement of energy efficiency and indoor environment through the integration of solar photovoltaic modules in daylighting louver systems

The global energy demand is predicted to increase significantly, with a strong link between energy consumption and CO2 emissions. Lighting energy accounts for a substantial proportion of total energy consumption in buildings, with the potential to be reduced using daylighting louvers. Despite some drawbacks, integrating solar photovoltaic modules with louvers could enhance energy efficiency while addressing the increasing demand for renewable energy systems in new buildings. Therefore, this study explores the potential benefits of integrating solar photovoltaic (PV) modules into a daylighting louver system to simultaneously reduce lighting, cooling, and heating loads and generate solar power. The performance of the proposed louver system was experimentally compared to a conventional building-integrated photovoltaic (BIPV) system installed on a window. The daylighting louver system demonstrated significant improvements in indoor illumination and occupants’ experience compared to the BIPV system. Furthermore, the indoor air temperature in the space with the louver system increased by about 5 degrees compared to the BIPV system, indicating its contribution to heating load reduction. The power generation performance of the louver system reached up to 65 % of the BIPV system, with a total cumulative electricity generation of 64 %. The results suggest that the daylighting louver system can be expanded to enhance its efficiency and address the increasing demand for renewable energy system installations in new buildings. Future studies should investigate the potential of 2-axis or 3-axis louvers to further improve the system’s efficiency.

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology

This study takes an old oceanarium in Jiangsu Province, China, as a case study and monitors the energy consumption values related to electricity, water and gas consumption for the whole year before the renovation of the oceanarium. Based on the energy consumption monitoring data, an in-depth analysis of the energy consumption defects of the oceanarium before the renovation is conducted using energy audit technology. We combine the characteristics of the oceanarium itself according to: (1) the results of the building energy audit; (2) the results of the envelope calculation; and (3) the latest energy-saving retrofit technology. We developed a more scientific building energy-saving retrofit programme. Using this method, we can accurately monitor the energy consumption problems in old oceanarium buildings and find building energy consumption defects. In this study, we monitor the real-time energy consumption of nine old government office buildings in Jiangsu Province, China using energy monitoring technology. We analyse and study the monitoring results and finally target the scientific energy-saving retrofits. This method can be used to carry out targeted energy-saving building renovation in the future and put forward new methods and ideas for studying the energy consumption of existing public buildings.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

Research on Adaptive Control Systems for Building Shading Based on Decision Tree and Random Forest Classification Algorithms

Dynamic shading systems for buildings, due to their variable mechanisms and high adaptability, have the potential to reduce energy consumption in buildings significantly. However, the performance of dynamic shading is largely influenced by the control system employed. This study aims to explore an adaptive control method for building shading based on decision tree and random forest classification algorithms (machine learning), with the objective of minimizing energy demands related to lighting and cooling as much as possible. The adaptive control system model may independently modify the location of building shading in response to input environmental conditions, thereby achieving energy savings for both lighting and cooling. According to the findings, the automated control system model may reduce building energy consumption by 38% overall, which is close to the ideal level of energy conservation.

Energy, exergy, environmental, and economic (4E) analyses of the usability of various nano-sized particles added lubricant in a heat pump system

The need for energy is rising significantly with the growth of technology in the world. This energy need is largely met by fossil fuels. The enhancement in their prices and the damage they induce to the environment, scientists have turned to alternative energy sources due to the depletion of fossil fuels. In recent years, these alternative energy sources have come to the fore as solar, wind, and wave energy. However, heating and refrigeration systems, whose share of energy consumption in buildings in the world is 40 %, can also compete with these alternative energy sources. In particular, heat pumps (HP) are at a level that can compete with renewable energy sources to seriously reduce this rate. In this study, different nanoparticles were added to the Polyol ester oil (POE) utilized in the compressor to enhance the performance of the HP. Thermodynamic, environmental, and economic performances of the obtained nanolubricants at different concentrations (0.5 wt% and 1 wt%) and flow rates (15, 30, and 45 g/s) were evaluated. The highest COP value of the HP was calculated as 4.14 at 0.5 wt% B-POE at 45 g/s. The best energy consumption in the HP was obtained with 0.5 wt% B-POE nanolubricant with a decrease of 10.96 % at 45 g/s compared to pure POE. The highest exergy efficiency in the HP was calculated at 0.5 wt% B-POE nanolubricant with a 13.53 % increase at 30 g/s compared to pure POE. The best exergoeconomic parameter (Rg,ex) performance was determined as 3.7148 kWh/$ in 1 wt% TiO2-POE nanolubricant at 45 g/s. The best enviro-economic value of 0.16182 ¢/h was obtained with 0.5 wt% B-POE nanolubricant at 45 g/s. In line with the results obtained, it was observed that the B-POE nanolubricant has a performance that can compete with the good-performing TiO2-POE nanolubricant.

Robustness Indicators for the Impact of Occupant Behavior Uncertainty on Building Energy Consumption

Robustness Indicators for the Impact of Occupant Behavior Uncertainty on Building Energy Consumption

Optimization and simulation of a novel multi-energy complementary heat pump system in cold regions

Solar-assisted ground source heat pump systems(SAGSHPSs) and solar- assisted air source heat pump systems (SAASHPSs) are recognized renewable energy technologies for clean and feasible heating and domestic hot water supply. However, in cold regions, the poor thermal comfort of SAASHPSs and the soil thermal imbalance of SAGSHPSs during heating periods must be resolved urgently. Therefore, to provide an efficient and stable heat source, a multi-energy complementary heat pump system(MECHPS) with seasonal heat storage was designed to meet the needs of building energy consumption. The system comprehensively utilizes solar energy and ambient air for seasonal heat storage during non-heating periods, and then directly provides heat coupled with the ground source heat pump during heating periods. The composition and control strategy of the system were introduced. The system was numerically modeled by the simulation software Transient System Simulation Program (TRNSYS), and the main MECHPS modules were experimentally verified. Additionally, to study the optimal capacity of MECHPS in terms of economy and energy savings, the Hooke-Jeeves algorithm was used for MECHPS capacity optimization with the objectives of minimising the annual cost and maximising the annual savings of standard coal. The results showed that compared with traditional SAASHPSs, the MECHPS exhibited better thermal comfort while maintaining the room temperature above 18 °C. Compared to SAGSHPSs, the MECHPS was able to achieve soil thermal balance, and its soil temperature remained largely constant after 15 years of operation. Finally, compared with the initial scheme, the annual cost reduction rate was 4.98 % under the annual cost optimization scheme and an additional 1310 kg of standard coal could be saved under the annual savings of standard coal optimization scheme. The proposed system provides a way to satisfy the building energy supply for widespread application in cold regions.

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm

Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm

A comprehensive study on the impacts of thermal comfort on occupants’ thermophysiology and cognitive performances in a radiant cooling environment using physiological measurements

Radiant cooling (RC) systems are increasingly implemented in public buildings to enhance human thermal comfort and reduce building energy consumption. The quality of the indoor thermal environment significantly impacts occupants’ learning and work performance. This investigation explores indoor occupants’ dynamic physiological regulation, thermal comfort, and cognitive performance under radiant whole-wall cooling (RWWC) systems. Twenty-eight participants underwent a summer exposure test task at varying ambient temperatures, yielding four collected data types: indoor physical parameters, physiological parameters (including skin temperature (ST), heart rate (HR)/heart rate variability (HRV), and electroencephalography (EEG)), performance index (PI), and thermal perception. Results indicate that the RWWC system notably improves thermal perception and comfort, with 93 % of participants reporting comfort at a target temperature of 24℃. Air temperature (AT) exerted the most significant effect on changes in ST on the right upper arm (p < 0.01) and left hand back (p < 0.01) at 30℃ compared to the 22℃. The difference in whole-body mean skin temperature (MST) is 2.68℃. The HR decline was more pronounced in higher temperatures, and the HRV representativeness index approached ‘1′ at 24℃. PI was affected by temperature and task type, displaying a non-linear correlation with AT (R2 = 0.6056) and an inverted “U” shaped curve with TSV (R2 = 0.9523). Thermal perception correlates with MST, HRV, and EEG. The study carries implications for the reasonable design of comfortable and healthy RC environments.

Developing an automatic integration approach to generate brick model from imperfect building information modelling

Recent advanced architectural algorithms, such as real-time optimization control and fault diagnostics, have garnered significant interest for their ability to reduce building energy consumption and improve equipment maintenance efficiency. However, deploying these algorithms in actual buildings often involves significant manual effort and time to align building data points with algorithmic requirements. In response, standardized ontologies like the Brick Schema have been introduced. This ontology streamlines the representation of building topology, equipment, and data points, as well as algorithm inputs, enabling quicker algorithm integration and deployment. Despite these advancements, manually constructing Brick models from scratch remains time-consuming, laborious, and prone to errors, presenting a new challenge in rapid model development. To overcome these issues, some researchers have turned to universal BIM formats, such as IFC/Revit, for accelerated Brick modeling. Nevertheless, the prevalence of errors in human-generated BIM models often impedes effective conversion. To efficiently convert imperfect IFC models into Brick models, this paper presents a novel BIM to Brick methodology comprising three key steps: IFC inspection and repair, IFC to Brick semantic modeling, and dataset mapping. This method ensures high-quality reuse of even imperfect IFC models. Our case study demonstrates that this method can produce an ideal Brick model from a flawed BIM model, meeting the semantic metadata requirements specified by the BuildingMOTIF tool. This innovative approach substantially reduces the effort required to generate Brick models, effectively handling the inaccuracies and noise inherent in real-world BIM.