Energy Performance Optimization Research Articles

Multiobjective optimization of daylighting, energy, and thermal performance for form variables in atrium buildings in China's hot summer and cold winter climate

Multiobjective optimization of daylighting, energy, and thermal performance for form variables in atrium buildings in China's hot summer and cold winter climate

Generative design of floor plans of multi-unit residential buildings based on consumer satisfaction and energy performance

Satisfying consumers is an important task in the design of residential floor plans. However, the design of residential floor plans with the aim of improving consumer satisfaction and energy performance has not been fully explored. Also, the preferences of different customer groups regarding customer satisfaction have not been studied. Traditional design and optimization methods still have limitations in terms of performance improvement, while performance-based generative design methods can explore the performance improvement potential of a larger range of solution spaces. Therefore, this study introduces a generative design framework that is grounded on optimizing energy performance and consumer satisfaction for multi-unit residential floor plans, which consists of four main processes: 1) Generation of floor plan schemes. 2) Evaluation of consumer satisfaction and energy performance of the generated schemes. 3) Running a multi-objective optimization to obtain optimal schemes. 4) The final floor plan scheme is determined by the decision maker. The proposed framework was employed in a real design case, and seven optimal solutions were obtained. Compared with the initial scheme, the maximum improvement in consumer satisfaction and energy performance was up to 38.78% and 11.18% respectively. The results also indicated that the preferences of different types of consumers differed significantly. Thus, different evaluation functions have been created to provide solutions with higher satisfaction levels targeting different types of consumers. As such, generative design and optimization of residential floor plans for specific types of consumers can be undertaken at an early design stage to improve consumer satisfaction and reduce energy consumption.

Investigating the impact of building materials on energy efficiency and indoor cooling in Nigerian homes

Urbanization, technological advancements, and lifestyle changes result in varying use of energy consumption throughout a building's life cycle. Building materials play a significant role in determining the energy efficiency of a structure, making it crucial to assess its energy consumption. The goal of the study is to investigate the relationship between building materials and energy efficiency and to assess their impact on interior cooling in Nigerian residential buildings, to provide insights and recommendations for optimizing energy performance and enhancing thermal comfort. The study relied on literature reviews, a case study, simulation and energy performance analysis of a typical three-bedroom apartment in three main distinct climatic zones of Nigeria to investigate the impact of building materials on energy efficiency and indoor cooling. The psychometric chart emphasised the importance of sun shading, dehumidification, and cooling (with potential humidification) for achieving thermal comfort while the advanced simulation capabilities of the DesignBuilder software provided valuable insights revealing that in Maiduguri, Minna and Lagos, the use of insulations on the building fabric and shaders resulted in a remarkable 37.9%, 33.4%, 27.6% reduction in the annual cooling load, and leading to a decrease of 4549 kWh, 4122 kWh, 3280 kWh, electricity consumption annually respectively. The study findings emphasize the importance of selecting energy-efficient building materials and implementing effective design strategies to enhance thermal comfort and reduce energy consumption. By implementing the recommended strategies, substantial energy savings can be achieved, resulting in reduced electricity consumption, cost savings, improved comfort and contributing to the advancement of sustainable building practices in Nigeria and beyond.

Strategies for LEED-NC-Certified Projects in Germany and Results of Their Life Cycle Assessment

The aim of this study was to investigate strategies for the certification of environmentally friendly office buildings in Germany. A total of 39 LEED-NC gold-certified office space projects were ranked according to their “optimize energy performance” credit (EAc1) achievements from the energy and atmosphere (EA) category and divided into two groups: 13 projects (group 1) with the highest and 13 projects (group 2) with the lowest EAc1 achievements. Nonparametric statistics were used to evaluate the differences between groups 1 and 2. A two-stage nested analysis of variance test was used to evaluate the differences between the two groups in terms of their life cycle assessment (LCA). A comparison of the two groups showed that group 1 outperformed group 2 in the EA category (p < 0.0001). However, in the other LEED categories, the differences between the two groups were not significant (p ≥ 0.0761). As a result, group 1 outperformed group 2 in terms of overall LEED points (p = 0.0048). The LCA of EAc1 showed that group 1 enacted the lowest environmental damage when compared to group 2 (p = 0.0040). The environmental assessment of LEED-certified projects can help green building managers choose the most sustainable certification strategy.

Experimental study on the fire characteristics of opaque and transparent solar photovoltaic panels

Although solar photovoltaic (PV) panels are widely used in buildings, previous studies mainly focused on optimizing energy performance and studies on their fire characteristics were limited, substantially affecting PV panel design and practical use. Therefore, the fire characteristics and applicability of three types of solar PV panels were assessed by combining experimental data and experts’ assessments. Under fire conditions, the temperature rising rate of the unexposed surface of the non-hollow transparent panel is 1058.3% and 47.2% greater than those of the hollow transparent and non-hollow opaque samples, respectively. The opaque sample's cracking start time and temperature are 186 s and 266.4 °C, respectively, around 44.5% and 41.8% of those of the transparent sample. The opaque sample showed the longest crack of 115.31 m, whereas the non-hollow transparent sample had the largest proportion of glass falling-off area of 8.76%. The fire risks of various PV panels differ under different scenarios. Experimental results and experts’ assessment indicated that hollow transparent PV panels with low fire risk could be used in buildings with high population density, while non-hollow opaque PV panels could be used in scenarios like PV power plants with less population; and the non-hollow transparent PV panels have a considerable fire risk, yet they can be employed in places with low safety requirements or no fire concerns. The obtained research outcomes can guide the selection of solar PV under various practical scenarios.

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

FedraTrees: A novel computation-communication efficient federated learning framework investigated in smart grids

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study

Today, an important problem of the building energy performance area is carrying out multi-criteria optimizations of real building designs. To solve this problem, a new method based on a meta-model is proposed in this study. Hence, the EnergyPlus™ is used as the simulation tool for the performance simulation of the building, then a couple of the multi-criteria Modified Coot Optimization Algorithm (MCOA) dynamically combined with the artificial neural network meta-models (ANN-MM) are employed. For the sample generation applied for training and validation of ANN meta-models, an optimum way is presented by this method to minimize the whole building energy simulations needed for their training, and validate precise results of optimization. Moreover, the method is used for the thermal comfort and energy efficiency optimization of a real house to achieve the optimum balance between the heating and cooling behavior of the case building. 12 effective design variables of this case study are selected. Also, the achieved results are put in comparison with the “true” Pareto front found through an optimization method based on simulation performed for more validation. It is assumed that 1280 points are adequate in this case study to obtain precise results on the Pareto set. Thus, 75% of the required simulations’ number based on physics has been saved by this size of sample considering the 5120 applied in the method based on simulation. Consequently, the optimum Pareto set of a real multi-criteria building efficiency optimization problem is achieved by the proposed method and accurate results are achieved.

Investigations on a bubble-pump-aided diffusion absorption heat transformer using deep eutectic solvent for harvesting and upgrading thermal energy

Absorption heat transformer shows promising potential in thermal energy harvesting and upgrading, while the corrosion and crystallization risks of existing working substance restrict its promotion. A bubble-pump-aided diffusion absorption heat transformer using deep eutectic solvent is proposed in this study. The influence of operating parameters of bubble pump is analyzed, indicating that motive head should be as large as possible while diffusion gas flow rate has the optimum. Because of worse pumping ability, deep eutectic solvent has narrower normal operating range compared with LiBr, but the optimal energy performance shows little difference. Ethaline exhibits the best performance among deep eutectic solvents. With heat source of 90 ℃, COP and ηEX of 0.401 and 48.9% can be reached to output thermal energy of 120 ℃, leading to a CO2 emission reduction of 20.1 Mt per year. Parametric analysis demonstrates that single-stage diffusion absorption heat transformer using ethaline can realize temperature lift up to 50 ℃, which delivers comparative performance against existing AHT systems, except for operating range. Approaches are discussed to enlarge its operating range and further improve its performance as well. The results in this paper contribute to: 1) providing a promising absorption heat transformer configuration for large-scale and long-term use; 2) quantitatively demonstrating the prospect of deep eutectic solvent; 3) enlightening insights in the recovery of industrial waste heat, the upgrading of thermal energy and the reduction of CO2 emission.

Method to identify an energy class for small wind turbines

Driven by the needs of the megawatt-sized wind turbine industry and the IEC 61400 standard set requirements, both the testing techniques and wind turbine selection for Small Wind Turbines applications can be expensive. Regulations have so far focused mainly on structural safety aspects, leaving out any optimization of energy performance. For this reason, this paper proposes a methodology to classify small wind turbines from an energetic point of view. For that, an energy class is defined using the normalized annual energy produced by a small wind turbine and is compared with its equivalent ideal one. The methodology is validated using two traditional methodologies and is applied to 22 small wind turbines, which have been tested by independent laboratories. The results show that the Energy Class is a useful tool for the selection of small wind turbines.

Life-Cycle Assessment of LEED-CI v4 Projects in Shanghai, China: A Case Study

The purpose of this study was to explore green office building certification strategies in Shanghai. The 45 LEED-CI v4 gold-certified office space projects were sorted by energy and atmosphere credit (EAc6, optimize energy performance) into two groups: 15 projects with the lowest EAc6 achievement (Group 1) and 15 projects with the highest EAc6 achievement (Group 2). To reach the gold certification level in Group 1, high achievement in EAc6 is associated with low achievement in two indoor environmental quality credits (EQc2, low-emitting materials, and EQc8, quality views), while in Group 2, low achievement in EAc6 is associated with high achievement in EQc2 and EQ8. For the life-cycle assessment (LCA), the functional unit was designated as follows: production (P) stage: production of building materials needed to ensure the requirements of EQc2 and EQc8 for 1 m2 of the building area; and operational energy (OE) stage: OE of 1 m2 of the building area over 50 years of the building’s lifetime. For the OE stage, two fuel source scenarios were used: 71.07% coal + 28.08% natural gas + 0.81% wind power (WP) + 0.04% photovoltaic (PV) (Scenario 1) and 50% WP + 50% PV (Scenario 2). The results of the LCA (P + OE) showed that under Scenario 1, the LEED certification strategy in Group 1 was greener than that in Group 2. When using Scenario 2, no differences were found between the two groups.

Energy-Efficient Opportunistic Spectrum Access in Multichannel Cognitive Radio Networks

We study an opportunistic spectrum access problem in multichannel Cognitive Radio Networks in which the secondary user (SU) has an energy cost when switching across channels. Channel switching from one RF chain to another also involves a reconfiguration delay that results in a shorter duration of transmission opportunity for the SU. For an imperfect sensing, we formulate the spectrum access problem in the framework of Partially Observable Markov Decision Process from which we obtain an average energy optimal policy. Also, we propose Efficacy - Efficient channel access with switching policy, for channel sensing and access based on the structure of the optimal policy. We compare the optimum policy with Efficacy, and show that Efficacy has a near optimal average energy performance.

Effect of earth-sheltering and atrium form and proportion integration on energy and lighting performance optimization in a hot arid climate of Mashhad, Iran

Effect of earth-sheltering and atrium form and proportion integration on energy and lighting performance optimization in a hot arid climate of Mashhad, Iran

A Multi-Objective Approach to Robust Control of Air Handling Units for Optimized Energy Performance

This paper presents a robust control framework with meta-heuristic intelligence to optimize the energy performance of air handling units (AHUs) and to maximize the thermal comfort of occupants by judiciously selecting the temperature set points of two controllers (i.e., the H∞ controller and the boiler controller). The selection of these set points is formulated as a multi-objective optimization problem, where the goal is to balance energy consumption with thermal comfort. Furthermore, the uncertainty weights of the H∞ controller are estimated to minimize oscillations in the outflow air temperature of the AHU plant. The performance of the proposed framework is investigated by considering the real-time weather data of Auckland, New Zealand. The results of the simulation show that the proposed robust control framework could significantly reduce oscillations in the outflow air temperature compared with the conventional case, where the temperature set points are selected empirically. Moreover, annual energy savings of 49.13% are achieved without compromising the thermal comfort.

LEED-CI v4 Projects in Terms of Life Cycle Assessment in Manhattan, New York City: A Case Study

Over the last decade, it has been clearly shown that the same achievements in Leadership in Energy and Environmental Design (LEED) projects can lead to different life cycle assessments (LCAs). However, the problem of contradictory achievements in LEED and LCA has not yet been resolved. This study aimed to identify and evaluate different strategies for LEED projects using LCAs. Thirty-nine LEED projects with the same characteristics—location and transportation, rating system, rating version, certification level, and space type—were collected and sorted by their energy and atmosphere (EA) category, “optimize energy performance” credit (EAc6) achievement into three equal groups (EALow, EAMedium, and EAHigh, where each group includes 13 LEED projects) to minimize the influence of uncontrolled factors on the LEED project strategy. The author focused on two extreme groups with very different EAc6 credit scores: EALow (13 projects) and EAHigh (13 projects). The groups were compared across LEED categories and credits. Wilcoxon–Mann–Whitney and Cliff’s δ test results showed that the EALow and EAHigh groups are associated with high/low achievements in materials-related credits such as “interiors life cycle impact reduction”, “building product disclosure and optimization—material ingredients”, and “low-emitting materials”. As a result, the EALow and EAHigh groups were reclassified into EnergyLow–MaterialsHigh and Energyhigh–MaterialsLow certification strategy groups. In this context, LCAs were used to assess the differences between the two strategies. The results showed that if natural gas was used for operational energy (OE), the EnergyHigh–MaterialsLow strategy showed lower environmental damage compared to the EnergyLow–MaterialsHigh strategy (p = 0.0635); meanwhile, if photovoltaic energy was used for OE, the EnergyLow–MaterialsHigh strategy showed lower environmental damage compared to the EnergyHigh–MaterialsLow strategy (p = 0.0036). The author recommends using the LEED protocol and the LCA method in parallel to better reflect the environmental impact of different certification strategies.

Identifying Energy Inefficiencies Using Self-Organizing Maps: Case of A Highly Efficient Certified Office Building

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.

Optical path model and energy performance optimization of aerogel glazing system filled with aerogel granules

Aerogel glazing system (AGS) is an advanced energy-efficient glazing system. According to Lambert-Beer Law, the optical path is a significant parameter that influences the optical performance of AGS, and thus influences the thermal-energy performance. However, the optical path of the granular aerogel layer in AGS has never been calculated before. In this study, a model was newly proposed to calculate the optical path of the granular aerogel layer. Firstly, the optical path of a single aerogel granule was calculated through geometrical optics. Then, the inhomogeneous distribution of aerogel granules was transformed to a homogeneous distribution through an equivalent model. Finally, the optical path of the aerogel layer was calculated by integration. The calculated optical path was utilized to simulate the spectral transmittance of different AGSs which were compared to the experimental results to validate the accuracy of the model. The results showed that the maximum difference between the simulated value and measured value of solar transmittance is 2.1 %. The results also indicated that the influence of the granule diameter on solar transmittance is slighter than the filling thickness. The energy performances of AGSs with different aerogel granules and different filling thicknesses were also simulated and evaluated under different weather conditions. The results showed that P1F16 (the diameter is 1 mm and the filling thickness is 16 mm) could reduce 22 % heat loss/gain when facing horizon and 10 % heat loss/gain when facing east and west in hot summer and cold winter region. The results also showed that P1F16 could reduce 17.36 % heat gain in the whole year in hot summer and warm winter region. It is also noticed that changing the aerogel granule’s diameter and the filling thickness has little effect on the energy performance of aerogel glazing system when faced north orientation.

A Study on the Optimization of Atrium Daylight and Energy Performance through Skylight and Shading Design in Commercial Buildings in Cold Zones

Atriums play an irreplaceable role within the plan of modern commercial buildings. However, the presence of skylights makes the atrium suffer from unfavorable conditions such as overheating, high energy consumption, and glare, which can be partially reduced by the sun-shading design of skylights. In this paper, we conducted a field test and questionnaire survey on the daylight quality of commercial buildings in cold zones in China to find out the current problems of daylight quality in atriums and to establish a model of commercial atrium. After the dynamic daylight and energy consumption simulation, the sensitivity analysis of daylight and energy performance is performed for the skylight and shading (SAS) design parameters under three shading types. Finally, the Pareto front solution set of optimized commercial atrium daylight, visual comfort, and energy performance for the three shading types has been obtained using a multi-objective search tool. The results show that SRR has the greatest effect on sDA, DGP, and EUI in all shading cases; while SGT has a greater effect on EUI than on daylight quality; FC has a greater effect on EUI than FV on EUI; and LS and LI have a more significant influence on daylight and visual comfort. The optimal SAS values of no shading, fabric shading and louvered shading were screened as SRR is 0.4–0.5, SGT is Double silver Low-E insulating glass; SRR is 0.5–0.6, SGT is Double silver Low-E insulating glass, FV is 0.5–0.7, FC is 0.5–0.6; SRR is 0.6–0.7, SGT is Double silver Low-E insulating glass, LS is 100 mm/125 mm, and LI is 60–70°. It is expected that this study will provide some information and reference for the design of commercial atriums in cold zones in the future.

Numerical analysis for energy performance optimization of hollow bricks for roofing. Case study: Hot climate of Algeria

In this century, the world is undergoing an energy transition led mainly by renewable energy and smart technologies. The realization of the energy-efficient building concept is a promising direction for global energy savings. This work investigates the benefit of incorporating three passive measures in hollow clay brick (HCB-8) structures commonly used in Algeria to improve thermal comfort and energy savings in buildings subjected to the climatic conditions of the city of Bechar. Ansys-Fluent software was used to study the heat transfer through different configurations of (HCB-8) integrated with a combination of two different passive measures. The results reveal that coating the inner surfaces of the brick’s cavities with a low emissivity coating, filling cavities with expanding polystyrene (EPS), or using phase change materials (PCM) improves the thermal inertia of hollow clay bricks. It can be argued that capric acid is a more effective PCM than n-Eicosane and RT-42. In addition, the study results show that the simultaneous incorporation of PCM and EPS into the brick can reduce the internal heat flux by about 73.7 % and shift the external heat wave in the indoor environment by about 5.5 h compared to traditional hollow clay bricks.

BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization

Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design.