Enhancing Building Energy Efficiency Research Articles

Enhancing Building Energy Efficiency: Leaf Transpiration Inspired Construction of Lignin-Based Wood Plastic Composites for Building Energy Conservation

Incorporating phase change materials (PCMs) into buildings represents a strategic method for reducing reliance on air conditioning systems and decreasing energy usage consequently. Inspired by leaf veins providing channels for transpiration, lignin porous carbon (LPC) was synthesized by chemical activation, showing a vein pore structure. The capillary network system of the LPC provided more channel to adsorb PCM through surface tension and capillary force, achieving an impressive loading capacity of LPC for polyethylene glycol (PEG) (75%), which demonstrated exceptional heat storage capability (133.21 J/g). The lignin-derived wood-plastic composite (L-WPC) was further prepared. With the gradual increase of LPC/PEG, the latent heat of L-WPC increased gradually. The ΔHm, ΔHc and energy storage efficiency of L-WPC-50 (incorporated 50 wt% LPC/PEG-75) reached 39.99 J/g, 42.65 J/g and 90.26%, respectively. The vein pore structure of LPC also provided a thermal conductivity link for L-WPC. L-WPC-50 demonstrated an increased thermal conductivity (0.66 W/(m·K)) and a reduced thermal diffusion coefficient (0.24 mm2/s), optimizing the effectiveness of the phase transition. Additionally, L-WPC-50 displayed satisfactory mechanical properties and dimensional stability, with tensile strength, bending modulus and water absorption thickness swelling rate measuring 14.40 MPa, 1043.72 MPa, and 1.61% respectively. It introduced a feasible approach for utilizing biomass in enhancing building energy efficiency, marking a significant stride towards reducing building energy consumption and advocating sustainable energy practices.

Towards High-Efficiency Buildings for Sustainable Energy Transition: Standardized Prefabricated Solutions for Roof Retrofitting

Enhancing energy efficiency in buildings plays a pivotal role in realizing the ambitious objective of achieving carbon neutrality by 2050, as outlined in the European Green Deal. Roofs represent the technical element most affected by energy phenomena related to heat transfer: in winter, roofing can lose up to 35% of heat, and the summer heat flux can even be higher. This paper provides a catalogue of optimized and sustainable solutions, with a specific focus on standardization and prefabrication principles, for enhancing the energy efficiency of the most prevalent types of roofs that characterize the national residential building heritage. The methodological approach that guided the research presented in this article was based on the identification and study of the most common roofings in the diverse national residential building heritage, followed by their classification according to their construction era. In the context of essential energy retrofitting of deteriorated residential building stock, 21 optimized standardized solutions have been identified. The outcome of performance evaluations of the proposed solutions allowed the implementation of a matrix that can be a valuable support for designers in selecting the most efficient precalculated and prefabricated solutions for the national residential building heritage based on energy performance and sustainability criteria.

Year-round experimental analysis of a water-based PVT-PCM hybrid system: Comprehensive 4E assessments

In this year-long study conducted in Cairo, Egypt, an innovative water-based photovoltaic-thermal (PVT) system coupled with phase change material (PCM) was evaluated for its ability to enhance building energy efficiency. The system utilizes 22 copper capsules filled with RT35 paraffin wax and a water-cooling duct to ensure optimal electricity generation and heat storage. The proposed system underwent detailed 4E (energy, exergy, economic, and environmental) assessments to compare its performance against traditional uncooled PV panels. The results indicated significant improvements: a reduction in cell temperature by up to 8.3 °C, an increase in electrical efficiency by 4.0–13.3%, and a boost in power output by 4.1–15.9%. Overall, the system demonstrated efficiencies between 27.23 and 67.33%, along with an exergy efficiency peak at 74.64% in July, substantially outperforming the uncooled panel. Economically, the PVT-PCM system achieved a 1.58-year payback period, and carbon emissions were reduced by 32%, highlighting its viability and environmental benefits. The potential of the PVT-PCM system to advance sustainable building energy management was strengthened by this study, providing a significant step forward in integrating renewable energy within architectural designs.

Enhancing building energy efficiency through a climate adaptive design: Considering upcoming extreme climate conditions

Known as one of the significant sources of impacting building energy efficiency, climate change can drastically alter the long-term energy consumption patterns of buildings. It is widely acknowledged in the literature that designs based on conventional pathways relying solely on historical weather data disregard the effects of the changing climate and lose their intended energy performance over time. Several endeavors have been made to generate weather files capable of capturing the essence of the changing climate and carrying its variability. In line with these efforts, the synthesized weather files have emerged as a promising alternative to the commonly used morphing method for incorporating climate change variability into building energy designs. However, a comprehensive framework with a potential to accommodate this variability in this context is yet to be investigated. Addressing this gap, the present study aims to propose a novel framework that can facilitate dynamic design upgrades in response to the changing climate including the extreme climate conditions. This framework has been applied to a case study building in Australia and around 31% annual thermal load reduction, on average, has been achieved. Furthermore, the results showed that conventional design pathways are likely to leave buildings inadequately prepared against the upcoming climate conditions. Therefore, the proposed framework is capable of generating a dynamic design that not only maintains the expected energy performance by incorporating the changing climate into its structure but also draws a more realistic picture of the building energy demand pattern over time.

DMFF: Deep multimodel feature fusion for building occupancy detection

The 2022 Global Status Report for Buildings and Construction (Buildings-GSR) indicates that construction activities have returned to pre-pandemic levels in most major economies, alongside more building energy consumption. To achieve the Net Zero emissions target by 2050, particularly in the post-pandemic era, accurate occupancy information is important to enhance building energy efficiency and improve occupancy comfort. While remarkable progress has been made in existing studies, they struggle to make full use of multi-sensor data to achieve high accuracy. Furthermore, there is a high expectation of the multimodel multi-temporary fusion by Transformer. In this study, we present a Transformer-based multimodal, multi-temporal feature fusion method (DMFF) for occupancy detection. To transfer domain knowledge from artificial intelligence into the building area, DMFF includes a pretrain-finetune pipeline and leverages pre-trained visual and sound models. Multiple pre-trained Transformer encoders are employed to extract features of different modalities. Then, we propose a self-attention mechanism for modality fusion to learn relationships among various sensors. Our DMFF method demonstrates superior performance on a real dataset, outperforming machine and deep learning methods (e.g., Convolutional Neural Networks, Random Forest, and Multilayer Perceptrons). Applied to a room setting, DMFF shows promising potential for building energy savings. The code and demo are accessible at https://github.com/kailaisun/multimodel_occupancy.

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

Thermal performance analysis of hollow bricks integrated phase change materials for various climate zones

AbstractThis comprehensive research addresses the significant challenge of building‐related energy consumption in Morocco. Our innovative approach involves integrating phase change materials (PCMs) into hollow bricks, strategically addressing the diverse climate zones prevalent in the country. The primary focus is on enhancing energy efficiency within structures. Leveraging detailed simulations and employing the enthalpy‐porosity approach, our study models the impact of PCMs on internal temperatures. Optimal outcomes are achieved by partially filling brick holes with PCMs in specific configurations, demonstrating the materials' ability to adapt to varying conditions. A noteworthy finding is the 2–3‐h phase shift observed in cold zones, indicating the potential for PCMs to effectively regulate temperatures. Equally significant is their capability to maintain a constant internal temperature of 26°C in hot zones, even amidst extreme external conditions reaching up to 47°C. This resilience underscores the novel and tailored thermal regulation potential of PCMs. Beyond the technical insights, our research highlights the paramount importance of considering regional climates in PCM applications' implementation. This awareness is crucial for optimizing energy performance in buildings and ensuring sustainability. In essence, this study contributes valuable knowledge and practical implications for the strategic deployment of PCMs to enhance building energy efficiency, emphasizing the need for context‐specific solutions in diverse environmental conditions.

Enhancing Building Energy Efficiency through Building Information Modeling (BIM) and Building Energy Modeling (BEM) Integration: A Systematic Review

With the ever-increasing population and historic highest energy demand, the energy efficiency of buildings is becoming crucial. Architectural firms are moving from traditional Computer-Aided Design (CAD) to BIM. However, nearly 40% of the energy consumption is due to buildings. Therefore, there is a need to integrate BIM with Building Energy Modeling (BEM), which presents an innovative opportunity to demonstrate the potential of BIM to minimize energy consumption by integrating building information software with data from existing energy-efficient building automation systems (EBAS). BEM is a form of computational analysis that can be used in conjunction with BIM or Computer-Aided Engineering (CAE) systems. In this paper, an attempt has been made to explore the existing literature on BIM and BEM and identify the effect of the integration of BEM in BIM in the design phase of the project. A recent survey from the last ten years (2012 to 2023) was carried out on Google Scholar, Web of Science, Science Direct, and Scopus databases. Inclusion/exclusion criteria were applied, and papers were scrutinized. From the results, it can be observed that the convergence of BIM and BEM is found to be useful in practical applications; however, projects with short life cycles might not be suitable for this solution. Challenges exist in the interoperability tools which have restrictions on data exchange. Binary translation is found to be the most suitable candidate for data exchange. The analysis further showed that the most used program for integrating BIM/BEM is Green Building Studio developed by Autodesk to improve construction and operational efficiencies.

Ranking building design and operation parameters for residential heating demand forecasting with machine learning

The European Union's Energy Performance in Buildings Directive has made significant strides in enhancing building energy efficiency since its inception in 2002. However, approximately 75% of EU buildings still fall short of energy-efficient standards. Furthermore, there is a growing momentum to extend the concept of nearly zero-energy buildings to entire districts, thereby fostering Net-Zero Energy Districts. This underscores the necessity for large-scale urban building energy modelling to identify and improve underperforming buildings and energy transition planning. Given the increasing interest in black box models for building energy performance, this study aims to identify common input variables in buildings energy demand literature, analyse their influence, and develop a heating demand prediction model using different algorithms: Random Forest, XGBoost, and Extra Trees. Four large datasets generated from white-box simulation in three Spanish cities were used for training and testing the models. Four features consistently stand out as the most important for prediction: shape factor, infiltration rate, south equivalent surface, and internal gains, regardless of the training algorithm or the climatic zone. The multi-location XGBoost algorithm with an optimizer emerged as the best-performing model, with an average Mean Absolute Percentage Error value hovering around 40%. Analysis employing SHapley Additive exPlanation (SHAP) values showcases the model's ability to identify factors that drive higher heating energy demand, alongside its strong predictive performance. This suggests potential integration of the model into programmes to identify key variables to be addressed during building renovation. Additionally, results show the potential of XGBoost-based software's potential for identifying building renovation targets.

Local thermal environment and thermal comfort of a novel low-temperature radiant structure using CFD simulation

A novel low-temperature radiant structure (LTRS) has been developed to create local thermal environment and thermal comfort. This design incorporates an air layer that separates the air-contact surface from the radiant cooling surface, which enables low-temperature radiant cooling, increases cooling capacity and reduces condensation risk. The study addresses the concern of whether the LTRS with low-temperature radiant cooling would degrade thermal comfort. Heat transfer models of the LTRS were developed, and agreed well with the experimental results with difference within ±0.3 °C. Four cases with radiant surface temperatures of 27.9, 15, 10 and 5 °C were investigated, and in all cases a consistent operative temperature was maintained. This study evaluated various parameters, including the air temperature and velocity distributions, mean radiant temperature, operative temperature, and thermal comfort indices. Results indicated that the application of the LTRS increased the supply air temperature by 1.6 °C, suggesting significant energy-saving potential ranging in 28.4 %–52.7 % compared to traditional all-air cooling systems. Moreover, both general and local thermal comfort under the regulation of the LTRS satisfied the criteria in the ASHRAE Standard 55. These findings demonstrate that the novel LTRS has the capability of improving energy efficiency while maintaining satisfactory levels of thermal comfort. This study provides valuable insights and serves as a reference for promoting the use of personalized radiant cooling systems and enhancing building energy efficiency.

Energy Measurement and Potential Energy Conservation Measures in Five Healthcare Buildings in Malaysia

Concern is growing around the world as a result of the global economy's overall rise in carbon emissions and energy consumption. The healthcare facility is among the buildings in the world with the highest energy use. Thus, it is necessary to create energy-saving plans that evaluate the energy performance of buildings and put energy-saving measures into practice. Enhancing building energy efficiency is most commonly achieved through a comprehensive energy audit. The Building Energy Index is the current method used to analyses all forms of building energy performance. The operating hours of a healthcare facility are longer than those of a commercial building. The issue emerges when there is no clear criterion for establishing whether or not a healthcare facility is energy efficient, and the present norm may misread healthcare energy consumption. To determine the actual onsite energy use at the healthcare building, a full energy audit is performed. Five places are selected based on the sort of healthcare services offered. According to the on-site complete energy audit, cooling equipment consumes the most energy. The study's findings may be divided into two categories: Healthcare Building Energy Index from detailed energy audit results and derived directly from power bills for the years 2018 and 2019. In terms of Healthcare Building Energy Index, there is a contrast between utilizing detailed energy audit data and using utility bills directly. Nonetheless, a comparison of Healthcare Building Energy Index with existing Malaysian requirements reveals a significant discrepancy. Based on the audit and analysis results, several savings opportunities may be presented.

Dual response multi-function smart window: An integrated system of thermochromic hydrogel and thermoelectric power generation module for simultaneous temperature regulation and power generation

Thermochromic smart windows have the capability to intelligently adjust color and light transmission based on ambient temperature and light levels, attracting considerable attention due to their potential to enhance building energy efficiency and residential comfort. However, excessive sunlight exposure can lead to increased heat generation, potentially affecting the service life of the window. In this work, we present the development of an efficient intelligent solar thermal utilization window, incorporating heat collection, temperature regulation, and power generation functionalities. The smart window achieves light modulation by adjusting the glycerol content in the thermochromic hydrogel. In addition, the thermoelectric material is arranged in series and parallel to the smart window functioning as the power generation module. The developed smart window shows excellent sunlight modulation capability (91.2 % visible light transmittance and 99.2 % sunlight shielding rate). Meanwhile, the response temperature of the window can be adjusted from 34.5 °C to 29 °C. The thermoelectric hydrogel can generate continuous and stable current (∼75 µA) with a temperature difference of 40 ℃. The practical application proves that the generated power by the temperature difference can fully meet the switching needs of small electrical appliances. This transformation framework between photothermal-electricity conversion deserves attention in the design of future smart windows.

Enhancing Energy Efficiency in Buildings through PCM Integration: A Study across Different Climatic Regions

In the quest for sustainable and energy-efficient building solutions, the incorporation of phase change materials (PCMs) into building envelopes emerges as a groundbreaking strategy. PCMs, renowned for storing and releasing thermal energy during phase transitions, stand as a promising avenue to curtail energy consumption while enhancing thermal performance. This study rigorously explores the potential energy savings and thermal comfort benefits achievable through PCM integration into building envelopes. Multiple energy simulations are conducted on a residential building model in diverse locations, including Irbid, Amman, and Aqaba in Jordan, and the city of Oradea in Romania, utilizing the EnergyPlus simulation tool embedded in DesignBuilder software v7.0.2.006. The results reveal that BioPCM®, derived from renewable biomass, significantly elevates thermal performance owing to its heightened latent heat of fusion. Optimal outcomes materialize with a PCM melting point of 23 °C, a configuration closer to the interior surface, and a thickness of 37.1 mm. The study underscores the superior performance in moderate climates (Irbid and Amman) compared to hot-dry climates (Aqaba) and cold-wet climates (Oradea, Romania). Financially and environmentally, incorporating PCM in Amman demonstrates potential annual energy savings of 5476.14 kWh, translating to a cost reduction of 1150 USD/year, and a decrease in GHG emissions by 2382.31 kgCO2eq. The estimated payback period for PCM incorporation in external walls is four years, robustly emphasizing the feasibility and multifaceted benefits of this energy-efficient solution.

Perspective for waste upcycling-driven zero energy buildings

This study proposes the novel concept of waste upcycling-driven zero energy building (W-ZEB). W-ZEB aims to accelerate the development of zero-energy buildings by incorporating waste upcycling processes (e.g., waste-to-energy (WtE), insulation material recovery, and biochar used in green roofs), ultimately advancing towards plus-energy building. W-ZEB includes three approaches: (1) generating additional energy through WtE, (2) enhancing building energy efficiency by utilizing advanced building materials recovered from waste, and (3) maintaining all elements and assets of W-ZEB using earnings obtained or saved from waste upcycling and management. To implement W-ZEB effectively, a building energy and waste management system (BEWMS) is suggested to coordinate and optimize energy demand, waste generation, and utilization, contributing to a net zero energy status and zero solid waste to landfills throughout building life cycle. Practical insights and research directions for unlocking the untapped potential of waste upcycling strategies to achieve zero-energy buildings are also provided.

Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption.

Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model

The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features—building compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution—we elucidate the intricate relationships between building characteristics and their corresponding heating load (HL) and cooling load (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of unique architectural designs and building materials. Using a dataset encompassing 768 buildings, we demonstrate the prowess of our proposed models. Among the algorithms we employed, the extreme gradient boosting algorithm stands out, registering impressive accuracy metrics (HL: RSQ = 0.9986, RMSE = 0.3797, MAE = 0.2467 and MAPE = 1.1812; CL: RSQ = 0.9938, RMSE = 0.7578, MAE = 0.4546 and MAPE = 1.6365). We further integrate SHAP analysis, revealing that relative compactness positively influences both HL and CL the most, closely followed by surface area and glazing area. By merging an explainable extreme gradient boosting algorithm with a Bayesian-based metaheuristic optimization technique, we ensure both high predictive accuracy and interpretability. This study holds profound implications for enhancing building energy efficiency, curbing waste, and championing the shift to sustainable energy sources, aligning seamlessly with SDG 7.

Geothermal energy system application: From basic standard performance to sustainability reflection

Shallow Geothermal Energy (SGE) systems are emerging as an attractive solution to enhance building energy efficiency and contribute to a low-carbon economy. This research investigates a SGE system comprising 40 boreholes installed in a university building located in Aveiro City under Mediterranean climate conditions. Both the building and the underground soil were monitored during the SGE operation, and EnergyPlus® software was subsequently employed for the integrated dynamic simulation. The building energy model was calibrated using field data, and a good agreement between the measured and the simulated temperatures was archived, with a Coefficient of Variance of the Root Mean Square Error (CVRMSE) below 10 %. The case study model performance was analysed and compared to other alternatives. The impact of the climate on the system performance was investigated by analyzing nine Portuguese cities situated in different climate contexts. A sustainability indicator was proposed, and an initial sustainability assessment was conducted. The results emphasize that the SGE system offered advantages compared to other alternative systems, achieving a 55 % reduction in heating and cooling energy consumption, and mitigating climate change by 405.67 ton of CO2 per 25-year lifespan. This study can guide further implementation of sustainable geothermal systems in Mediterranean climates.

Optimization of the thermal-optical performance of a PCM-integrated thermochromic glazing system

Facades that employ adaptive technologies have shown great potential in enhancing building energy efficiency. This study investigates a novel phase-change material integrated thermochromic glazing (PCM-TCG), and applies it on skylights of an exhibition building in Shanghai. Thermal performance and daylighting performance of the building were analysed using Fluent and Grasshopper/Honeybee respectively and they were optimized through Non-dominated Sorting Genetic Algorithm-II. It was found that on summer solstice and winter solstice, the optimized solution provided an excellent trade-off between thermal and daylighting performance, reducing the total energy demand by 88% and increasing the daylighting satisfaction time by 5 hours compared to low-E glazing on summer solstice and winter solstice.

A Statistical Analysis of Energy Consumption Survey of Public Buildings in a Hot Summer and Cold Winter Coastal Zone of China

Public buildings consume the largest proportion of total energy consumption and carbon dioxide emissions in the building sector of China. Enhancing building energy efficiency becomes a necessary way to reduce greenhouse gas emissions and energy waste. In this study, 10-year real data from 2000 buildings covering five different types of public buildings in a hot summer and cold winter zone in eastern coastal China were investigated to analyze energy-saving potential (ESP) and the impacts of various influencing factors on building energy performance. The concept of energy consumption limit (ECL) was proposed and used for identifying high energy-consuming buildings and the calculation of ESP. Hotels, shopping malls, and office buildings were the top three types of buildings with high ESP. ESP for the high energy-consuming buildings based on the benchmark limits was more than 18%, and that for three-star hotels and shopping malls was 51%. In addition, a correlation analysis between energy consumption and influencing factors was carried out, which laid the foundation for the development of building energy performance assessment and diagnosis tools.

Non-invasive human thermal comfort assessment based on multiple angle/distance facial key-region temperatures recognition

Accurate monitoring of human thermal comfort is crucial in optimizing HVAC system control scheme and enhancing building energy efficiency. The method based on facial temperature infrared recognition is a non-invasive and real-time thermal comfort predictive strategy, which has been proven great application potential. In response to the limitations of previous fixed-position facial infrared detection in practical applications, this study aims to explore a less restrictive non-intrusive method for assessing human thermal comfort. The temperature features of multiple regions on human face are identified by an infrared camera, and six temperature-sensitive regions are selected through skin thermal sensitivity analysis. Thirty subjects participate in a series of field experiments. To reduce the measurement limitations caused by human postures, the correlation between these six facial regions, indoor air temperature, thermal sensation votes (TSV), and thermal comfort votes (TCV) are analyzed in details to determine two key facial regions for the non-intrusive thermal comfort assessment. By applying the YOLOv5 algorithm, the real-time extraction of facial region temperatures from multiple angles and distances is achieved. On this basis, a data-driven thermal comfort predictive strategy based on facial temperature and optimized SVM model is designed. Results indicate that the method achieves a general accuracy of 85.68% in predicting thermal comfort based on its successful recognition rate of 88.7% for facial key regions.