Energy Utilization In Buildings Research Articles

Assessing opportunities for enhanced lighting energy conservation via occupancy and daylight monitoring

Efficient energy utilization in buildings is crucial for sustainability. This work proposes an intelligent system that leverages computer vision techniques and CCTV images to assess indoor lighting energy usage based on occupancy, artificial lighting, and daylight conditions. Object detection models - You Only Look Once (YOLO) version 3 (v3) and v8 are employed to detect people, lights, and windows, achieving promising accuracies of 94.9 ​%, 73.3 ​%, and 98.7 ​%, respectively. The system categorizes scenarios as energy-efficient, wasteful, or neutral by integrating these outputs, highlighting opportunities for improving efficiency by harmonizing lighting infrastructure with occupancy and daylight exposure. Performance analyses, including training and validation metrics, are presented. This study demonstrates the potential of computer vision and AI for optimizing energy utilization, enabling sustainable building operation and promoting energy-positive occupant behaviors through sensor-driven methodologies.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Thermal characteristics of heat-pipe-ring embedded building façades as both building-integrated and building-attached solar collectors

Solar energy utilization in buildings is crucial for energy conservation and carbon reduction. Previous studies have shown the promise of heat-pipe-ring (HPR) embedded building façades as effective modules for solar energy utilization and energy conservation in air-conditioning systems. However, research on their thermal characteristics as building-attached solar collectors is limited. This paper fills this gap by combining experimental data and numerical simulations using self-developed Fortran programs. Regression analysis of HPR modules in building integration solar systems (BISS) determines thermal efficiency and thermal loss coefficient. Results indicate the advantage of selective Cr coating in minimizing energy losses, with less pronounced impact from glass covers. Optimal HPR module designs for building-attached solar systems (BASS) in different Chinese climate regions are established, achieving yearly averaged thermal efficiencies ranging from 62.6 % to 73.5 %. The study further presents the thermal performance of HPR modules in BASS on a typical summer day in each city within each climate zone. These findings offer practical insights for selecting HPR modules in BASS systems tailored to diverse regions, promoting the integration of renewable solar energy into building applications.

Application of Photovoltaic and Solar Thermal Technologies in Buildings: A Mini-Review

Buildings account for a significant proportion of total energy consumption. The integration of renewable energy sources is essential to reducing energy demand and achieve sustainable building design. The use of solar energy has great potential for promoting energy efficiency and reducing the environmental impact of energy consumption in buildings. This study examines the applications of photovoltaic and solar thermal technologies in the field of architecture, demonstrating the huge potential of solar energy in building applications. To ensure a fresh and thorough review, we examine literature that encompasses the advancements made in the utilization of solar energy in buildings over the past decade. The key factors to consider in this study are reliability, performance, cost and aesthetics in real applications of photovoltaic and solar thermal technologies in the field of architecture, which have a significant impact on people’s acceptance of solar energy technology. Recent developments in feasible and effective optimization solutions for solar energy technologies are summarized. Accurate and convenient simulation techniques are also summarized for reference. The results show that the rapid progress of BIPV systems is fueled by advancements in three crucial areas: enhancing solar cell and module efficiency, reducing manufacturing costs and achieving a competitive levelized cost of electricity. The results can provide researchers with a reference for understanding recent technological developments in the integration of solar energy into buildings.

Machine Learning Applications for Smart Building Energy Utilization: A Survey

The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.

Predicting energy consumption of mosque buildings during the operation stage using deep learning approach

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

Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms

The consumption of energy in buildings holds considerable importance within the realm of overall energy usage. This underscores the critical nature of employing efficient strategies for managing energy. Accurate assessments of energy consumption in buildings serve as a central factor in improving energy efficiency and providing guidance for energy management choices in the context of residential buildings. Therefore, predicting and optimizing building energy utilization has become a popular area of research because it has the potential to greatly improve how efficiently energy is used in buildings. In this study, in the first step, four different models, including three artificial neural network frameworks and a regression model are expanded to predict cooling and heating loads. After selecting the best network, using four optimization techniques, the hyperparameters of the selected network are tuned and the best hybrid model is obtained. Furthermore, the multi-objective optimization process is extended to define the optimal conditions using Particle Swarm Optimization, and Biogeography-Based Optimization optimizers and LINMAP, TOPSIS decision-making approaches. The findings of this investigation underscore the enhanced efficiency conferred by the BBO algorithm on the Extreme Learning Machine. Specifically, an increase in the correlation coefficient from 0.9959 to 0.9969 for cooling load estimation and from 0.9973 to 0.9993 for heating load estimation reflects the improved alignment of results from the ELM-BBO model with actual experimental data. These values surpass those of all other models, indicating that the ELM-BBO model demonstrates the best performance among the hybrid models. Importantly, the results obtained underscore the overall effectiveness of the selected optimizers in delivering accurate outcomes.

Smart Deep Learning Model to Recognize PCM Optimization Performance on Solar Cooling System

Phase Change Materials (PCMs) offer a significant advantage by reducing the need for multiple cooling systems, potentially revolutionizing thermal comfort in buildings and optimizing thermal energy storage. The intriguing prospect of integrating PCMs into active heating and cooling systems has garnered considerable attention. Furthermore, the compatibility of PCMs with photovoltaic (PV) systems and various renewable energy sources enhances the system’s efficiency. This study explores the promising application of PCMs in solar-powered cooling systems, demonstrating their capacity to improve thermal comfort and energy efficiency. The choice of PCMs with specific phase change temperatures and heat of fusion is pivotal for optimal system performance. The integration of PV systems with thermoelectric coolers and other renewable energy sources further enhances the overall sustainability and energy utilization in buildings and cooling systems. These findings underscore the potential of PCM-based technology in addressing thermal energy storage challenges and advancing the sustainability of cooling systems and building environments.

Transfer Learning in the Transformer Model for Thermal Comfort Prediction: A Case of Limited Data

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building’s energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results.

Dual‐mode smart flipping materials and devices for thermal management

AbstractThermal management is of great significance for human activities. Attaining thermal comfort not only requires thermal control of building's macroenvironment, but also additionally personal thermal regulation. Radiative cooling technologies are anticipated to effectively lower building energy utilization and provide optimal thermal comfort for individuals in hot weather. Nevertheless, these static and single‐purpose characteristics lack the ability to adjust to rapidly changing weather conditions, often leading to excessive cooling. To overcome this challenge, the emergence of dual‐mode smart flipping technologies has provided a pathway for dynamically adjusting the heating or cooling of objects in response to fluctuations in ambient temperature. First, the underlying principles of dual‐mode smart flipping are shown. Then the evolving materials and approaches of smart flipping are given an introduction to adapt to different environments under external stimuli, such as mechanical flipping, temperature, humidity, and so forth. Afterward, we present the recent applications of dual‐mode smart flipping materials and devices in personal thermal management, thermoelectric generation, energy‐saving buildings, and smart windows. Finally, the challenges and outlook of dual‐mode smart flipping are presented and future directions are identified.

Building Energy-Saving Technology

Buildings consume about 40% of the global energy. Therefore, the building sector plays a key role in achieving the goals of carbon peak and carbon neutrality. Various energy-saving technologies for buildings, such as building envelops, mechanical systems, and energy resources, have been developed to help to achieve zero- or even net-energy buildings while maintaining comfort and a healthy indoor environment. This Special Issue on building energy-saving technology was open to all contributors in the field of building engineering. The original experimental studies, numerical simulations, and reviews in all aspects of building energy utilization, management, and optimization have been considered. For this event, all of these topics were covered in the extensive submissions which were accepted, but interesting papers on other aspects of building energy efficiency were also received. The purpose of this editorial is to summarize the main research findings of the accepted papers in this Special Issue, including the energy-saving technologies involved in building envelops, mechanical systems, and occupant behaviors, and to identify a number of research questions and research directions.

High-resolution spatial assessment of the zero energy potential of buildings with photovoltaic systems at the city level

Zero energy buildings are considered as a viable way to reduce energy use and CO2 emissions in the building sector. This study proposes a high-resolution assessment framework by combining top-down and bottom-up approaches to evaluate the zero energy potential of buildings with photovoltaic systems at the city level. We selected Shanghai as a case study to validate the practicality and feasibility of the proposed framework. Four energy scenarios were further designed based on the location of the installed solar photovoltaic panels (i.e., building roofs and facades) and the window-to-wall ratio. The results show that most buildings in Shanghai's central area show a high potential for zero energy consumption. However, buildings located in Shanghai's suburbs have difficulty in achieving zero energy if only solar photovoltaic systems are used. In summary, the substitution rate (i.e., the ratio of the coal equivalent of solar energy potential to building energy consumption) for the four scenarios ranges from 25.74% to 53.13%. This indicates that the application of solar energy received by buildings can reduce more than half of Shanghai's building energy consumption during the operation phase. The proposed framework may assist policymakers in developing better energy conservation strategies and improving solar energy utilization in buildings.

Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

SummaryThere are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over‐estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.

Using Simulation to Maximize the Solar Energy Utilization in Buildings in Amman, Jordan

This paper discusses the potential for reducing the energy consumption of residential apartments built on slopes in Amman, Jordan, by proposing new regulations to ensure adequate access to solar radiation. The energy-conscious concept has appeared around the world with rising oil prices and a rise of high residential buildings. It is necessary to consider the possibilities of maximizing the use of solar energy in a building through an architectural design strategy. This leads to mandatory regulations at the urban planning and building design level. This paper provides an overview of the Greater Amman Municipality (GAM) regulations in force regarding the availability of sunlight and achieving a healthy building environment. Analytical scientific methods were used in the work. Computer simulations were performed using a parametric design approach to make new decisions to establish new height and setbacks dimensions for apartment buildings in Amman. The present study provides workable guidance based on determining the optimal distance between condominiums on sloping ground, through which both the architects and the urban designers will be able to define the number of floors that will receive sufficient natural lighting.

Impact of Phase Change Materials on Cooling Demand of an Educational Facility in Cairo, Egypt

Heat gains and losses via building envelopes are impacted by varied characteristics such as geometry, orientation, properties of the building materials, and the type of construction and its interface with the exterior environment. Current studies are investigating the use of phase change materials (PCMs) characterized by high latent heat and low thermal conductivity that may cause temperature time lag and reduce amounts of heat transferred through building envelopes. The prime objectives of this research are evaluating zones’ energy consumption by type for an educational facility in a dry arid climate, examining the effects of a PCM (RT28HC) and polyurethane insulating material, comparing these effects to the existing situation with respect to cooling energy savings and CO2 emissions, and studying the effect of varying PCM thicknesses. The working methodology depended on gathering the real status and actual material of the building, constructing models of the building using Design Builder (DB) simulation software, and comparing the insulation effect of incorporating polyurethane and phase change insulating materials. A parametric study evaluated various PCM thicknesses (6, 12, 18, 24, 30, and 36 mm). Validation was performed primarily for a selected year’s energy usage; simulation results complied with field measurements. The results revealed that an 18 mm PCM had a high efficiency regarding thermal comfort attributes, which reduced cooling energy by 17.5% and CO2 emissions by 12.4%. Consequently, this study has shown the significant potential of PCM regarding improved energy utilization in buildings.

Seasonal performance comparison analysis of the roll-bond photovoltaic thermal heat pump system in a multi-energy generation: An experimental study

Solar photovoltaic thermal heat pump systems contribute significantly to the building industry's goal of energy efficiency and “carbon neutrality” by producing multiple types of energy from the intensive use of solar energy to meet the varied and dispersed energy requirements of modern buildings. An experimental investigation of the roll-bond photovoltaic thermal heat pump system powered by a roll-bond photovoltaic thermal unit is presented in this study, together with the system's precise operating principle and the structural design of the unit. The theoretical underpinnings of experimental research are discussed, including the establishment of test platforms and error analysis, as well as the method for evaluating system performance, which includes solar power, heating, and cooling. Following that, continuous experiment monitoring under various weather conditions in Dalian is carried out throughout the year, which serves as the basis for system performance evaluation and comparison, with a focus on heating, power, and cooling. Comparison analysis results show that the heating and power generation performance in the summer season is the highest, with an average photovoltaic efficiency of 13.6% and an average heating coefficient of performance of 6.16, which is followed by the transition season and winter season, with an average photovoltaic efficiency and heating coefficient of performance of 11.9% and 6.05, 10.2% and 4.54, respectively. However, the performance of power and heating cogeneration in summer conditions fluctuates due to variations in solar radiation intensity. Furthermore, the system can run in cooling mode during the night, on overcast days, and on rainy periods to generate cooling capacity. The cooling performance is the highest during clear nighttime, followed by the overcast nighttime and overcast daytime, with an average cooling coefficient of performance of 2.99, 2.66, and 2.12, separately. Rain, as a cooling water source, significantly improves system cooling performance. The results show that the proposed roll-bond photovoltaic thermal heat pump system can generate high-efficient multi-energy under a variety of weather conditions while remaining stable over time, providing a valuable practical feasibility reference for renewable energy utilization in buildings in northern China.

Effect of charging/discharging temperatures upon melting and solidification of PCM-metal foam composite in a heat storage tube

Solar energy has addressed the significant issue of energy conservation and emission reduction through providing an alternative solution to the building energy utilization. To solve the inherent intermittency problem, latent heat phase change module is designed to even the fluctuation of energy supply. This paper designs a phase change heat storage/release unit and builds a visual test system for phase change heat storage/release. To study the influence of different heating and cooling temperatures upon thermal cycle of melting and solidification, a series of experiments are carried out. Melting front evolution, temperature field and response, and uniformity analysis are assessed. Experimental results demonstrate that the phase interface changes from top to bottom in the melting process. However, solidified covers appear evenly up and down during solidification process. Reasonably increasing the heating temperature effectively shortens the complete melting time. 56.0% reduction in full melting time is achieved if increasing the heating temperature from 65 °C to 85 °C. However, this is done at the expense of decreasing the temperature uniformity of the PCM. The results proved guidance for the design strategy on passive building energy utilization.

Light transmitting glass fiber reinforced cementitious composite containing microencapsulated phase change material for thermal energy saving

The energy utilization for artificial lighting, cooling, heating, and air conditioning in buildings results in the release of greenhouse gases and causes climate crises. In this regard, a novel light-transmitting cementitious composite (LTCC) was developed by substituting microencapsulated phase change material (MPCM) to reduce the building energy utilization via transmitted light through the composite and improve the solar thermal energy efficiency. The cementitious matrix is produced with cement, kaolin, silica sand, water, superplasticizer, adhesive polymer, glass fiber, different ratios of MPCM, and plastic optical grids to let inward sunlight transmittance. The current study thoroughly investigates the characteristics of light-transmitting composite, including MPCMs, via physico-mechanical, chemical, microstructural, thermal, light transmittance and solar thermoregulation tests. The thermal conductivity of the composite with 0 wt% MPCM decreased from 1.09 W/mK to 0.96 W/mK with 15 wt% MPCM addition. Incorporating 10 wt% of MPCM reduced the 28 days-compressive strength of specimens by almost 28 % due to the lower strength and density of microcapsules, as well as the voids formed by damaged MPCMs. On the other hand, the incorporation of MPCM did not dramatically affect the flexural strength of the cementitious composite. DSC analysis results revealed that the composite containing 15 wt% MPCM shows a latent heat of fusion of 14.6 J/g with a melting point of 17.65 °C. FTIR analysis disclosed that MPCM maintains its chemical structure in the composite. Composite slabs exhibited up to 12.4 % artificial light transmittance, which would translate to a considerable increase in the lighting efficiency of commercial and residential buildings. Thermoregulation performance test under ambient conditions indicates that the specimen containing 15 wt% MPCM can provide a cooler room temperature for 6.5 h when the room or surface temperatures increase above 21–23 °C, and a warmer room when the temperature decreases below these temperatures. The findings of the current study can be applied to enhance thermal energy saving and artificial lighting efficiency in buildings that inspire the design of environment-friendly constructions.

Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency.

Numerical approach for sizing vertical ground heat exchangers based on constant design load and desired outlet temperature

Vertical ground heat exchangers (GHE), as part of a ground source heat pump system, have been utilized for energy extraction/injection from/to the ground, i.e. a renewable source of energy, in the heating/cooling months to reduce building energy utilization. The current available GHE sizing methods are mostly based on several over-simplified one or two dimensional analyses, which cannot predict its thermal behavior with the accurate and realistic boundary conditions or in series configurations. In this study, thermal performance of vertical GHEs is numerically investigated at a given constant load through solving full transient 3D Navier-Stokes equations inside the U-tubes and the energy equation all over the computational domain, i.e. both fluid and solid parts. There are three interconnected parameters, namely GHE load, the heat transfer fluid outlet temperature, and the GHE depth that influence its thermal performance and consequently, the required investment cost. Therefore, the thermal performance of GHEs is computed for different constant loads at different depths, and it is shown that at each constant load, there is a trade-off between the investment cost and the thermal performance. Furthermore, the GHEs thermal performance in parallel and series configuration is investigated at a fixed depth and different loads. In light of the obtained data, the required size of the GHEs for a test building with an imbalanced energy extraction/injection rate is derived and the thermal performance of the proposed cluster of GHEs is numerically simulated for 20 years.