Potential Energy Savings Research Articles

Multi-objective optimal energy-efficient retrofit determination using hybrid urban building energy model: Considering uncertainties between models

AbstractTypical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty. To solve it, this study proposes a decision support framework that integrates a hybrid urban building energy model (UBEM) method, NSGA-II, and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty. The study took the building groups in Sipailou campus as a case study and identified 76 “stable solutions” and 149 “active solutions” that minimize energy consumption, carbon emission, and life-cycle cost (LCC) over 30 years from 40,353,607 retrofit schemes. Key findings include that when considering model uncertainty, the quantities, types, and ranks of optimal retrofit solutions have changed. When the error of baseline UBEM validation is within ±5% and considering uncertainty transmission from energy simulation to ANN model, the energy-saving potential of optimal retrofit schemes has expanded from [63.78, 65.05]% to [60, 68.75]%, carbon-saving potential has shifted from [63.69, 64.09]% to [59.92, 67.79]%, and the LCC has changed from [−40.68, 14.59] × 106 to [−38.25, 16.97] × 106 Yuan. This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.

Impact of Building Orientation on Energy Performance of Residential Buildings in Various Cities Across Afghanistan

Building or solar orientation, a key architectural design parameter, significantly influences energy consumption in buildings. Optimizing building orientation to harness passive solar benefits is a fundamental and cost-effective measure in designing energy-efficient buildings. However, the optimal orientation varies based on geographical location, climatic conditions, and building type. Notably, Afghanistan’s building sector currently lacks tailored energy efficiency regulations. Therefore, this study investigates the impact of building orientation on the energy performance of residential buildings across nine cities in Afghanistan, each characterized by distinct climatic conditions and geographic locations, employing BEoptTM energy simulation software. The findings reveal diverse optimal orientations, dividing the country into three distinct climatic zones: subarctic (optimal orientation: south-southeast), continental (optimal orientation: south), and hot-arid (optimal orientation: north). The optimal orientations in these regions yield potential energy savings ranging from 25.6% to 48.9% compared to the least efficient orientations. These insights are critical for establishing location-specific building regulations in Afghanistan, promoting energy-efficient design, and addressing the country’s current trend of unsustainable development.

Multi-objective optimization of building envelope of rural housing in the severely cold region of China based on energy consumption, thermal comfort and cost-effectiveness

The performance of rural residential buildings has a great energy-saving potential due to the great spontaneity and ignorance of the rural residential construction. To solve this problem, this paper provides a multi-objective optimization model based on Chebyshev inequality. The model takes energy consumption, indoor thermal comfort, project cost as optimization objectives and considers operating conditions in winter and summer. This study taking a rural residential house as an example and analysed the influence of its envelope parameters on building performance using EnergyPlus. The parameters of the building envelope were optimized, which verified the superiority of the method. Optimization results showed that the heating energy consumption was reduced by 29%, and the air conditioning energy consumption was reduced by 6%. This study analysed the relationship between the building energy consumption, indoor thermal comfort and the cost by multi-objective optimization method, to provide a new research idea for rural residential research. Optimal design solutions were generated under various weighting coefficients. The model aimed to meet the climatic and economic conditions of northern China and similar regions. Therefore, based on these optimization results, decision-makers can choose optimal design and construction solutions according to their own will.

Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives.

Innovative Photovoltaic-Aeration Integration: Enhancing Energy Efficiency and Grid Stability in Wastewater Treatment

Abstract This paper presents a detailed investigation into enhancing the energy efficiency of wastewater treatment plants (WWTPs) by integrating photovoltaic (PV) systems, emphasizing power flow analysis and experimental validation. Recognizing the substantial energy demands of aeration processes in WWTPs, this study proposes an innovative integration of PV panels with aeration tanks. This approach generates renewable energy and optimizes energy use through the thermal interaction between the PV panels and the aeration tanks. Key findings demonstrate a 15% overall increase in energy efficiency and a 5% improvement in PV efficiency due to aeration-induced cooling, along with a reduction in voltage fluctuations by up to 30% during high-demand periods. Additionally, the integration offsets approximately 20% of the WWTP's total energy consumption. The research is structured into two main components: a comprehensive power flow study using DIgSILENT PowerFactory and a laboratory experiment to validate the integration's effectiveness. The power flow analysis evaluates the electrical impact of PV integration on the WWTP's power grid, focusing on scenarios such as load fluctuations, grid disturbances, and the synchronization of PV generation with plant energy needs. The simulation results indicate that the integration significantly enhances the stability and efficiency of the plant's electrical system, reducing reliance on traditional energy sources. Concurrently, a laboratory experiment explored the practical effects of integrating PV systems with aeration tanks. The experiment demonstrated that the cooling effect provided by the aeration tanks leads to increased PV efficiency and notable energy savings. These experimental results align with the simulation findings, confirming the efficacy of this integrated approach. This study introduces a novel methodology for integrating renewable energy technologies into industrial processes, showcasing the potential for significant energy savings and improved operational efficiency in WWTPs. Future research will focus on scaling this integration strategy and assessing its long-term impacts on energy efficiency and wastewater treatment effectiveness.

User behavior and energy-saving potential of electric washing machines

With the intensification of the global energy crisis and the increase in environmental awareness, energy-saving problems related to household appliances have garnered widespread attention. Here, the usage patterns of electric washing machine users and their energy-saving potential was mainly explored, so as to improve the current situation that the influencing factors of existing research behaviors were not deep enough and the energy saving potential was not specific enough. A questionnaire survey was used to gather information on 20,840 users, including individual characteristics, energy-saving awareness, and usage behavior. The study analyzed the differences in users’ energy-saving awareness and behavior through a series of analysis methods, and evaluated the energy-saving and water-saving potential of electric washing machines. The results showed that user behavior such as washing mode, washing temperature, and the volume ratio of clothes significantly affected on the energy and water consumption of electric washing machines. Individual characteristics of users such as gender, age, educational background, and family income were strongly correlated with their awareness of and decisions made regarding energy conservation. Improving the energy efficiency of electric washing machines and optimizing user purchasing behavior could result in 38,787.54 GWh national energy savings potential, and 6.90 million tons of water-saving potential. This study will help manufacturers and government departments better understand consumers’ usage behavior regarding electric washing machines, which could allow them to modify their market strategies and bolster the promotion and education of energy efficiency labels for electric washing machines. This also could support the nation’s objectives for environmental preservation, water and energy conservation, and the sale of products with lesser energy efficiency.

Exploring energy efficiency and savings potential of a horizontal domestic drain water heat recovery system in high-rise apartment buildings

In residential buildings, domestic hot water (DHW) used for showering and handwashing produces significant amounts of hot drain water (DW), which is typically wasted. This study proposes a horizontal drain water heat recovery (DDWHR) unit to capture waste heat from DW and improve overall DHW system efficiency with the focus on thermal exchange in a compact installation suitable for high-rise apartment buildings. Experiments under different operating conditions were conducted to evaluate the DDWHR unit’s thermal performance and energy-saving potential. Results showed that the proposed DDWHR unit achieved energy efficiency of 90–95%, depending on operating conditions, due to high heat recovery rates. In the entire DHW system, the DDWHR unit demonstrated approximately 15% heating energy savings compared to conventional DHW setups. Also, nationwide adoption of the DDWHR unit in two-person apartments could reduce gas costs by about 14% annually. The findings of this study indicate that the horizontal DDWHR unit can effectively promote sustainable DHW use in high-rise residential buildings and offers a promising solution for improving energy efficiency in DHW systems.

Energy-saving and carbon reduction analysis of series/parallel combination for heat pump and chiller in a hotel building

Taiwan is known for its hot and humid climate, where buildings rely on air conditioning systems to provide comfortable conditions. Specifically in commercial buildings such as hotels, apart from being required to provide comfortable indoor conditions, they need to provide hot water 24 h for guests, leading to the common application of chiller and heat pump systems in hotels. With the rise of the Taiwanese tourism industry, many hotels have been newly built or renovated. However, during the planning and construction stages, considerations may be limited by personnel experience, operational difficulties, installation costs, or the desire to simplify operations. This often results in the separate consideration of heat pump systems and chiller systems, leading to unnecessary resource and energy waste. Therefore, this study takes place at a hotel building in Taichung located in Central Taiwan. Designing a series/parallel system between the heat pump and chiller systems. Through a control system and a real-time recording system, controllable factors were analyzed, such as system equipment supply/return water temperature, outdoor relative and enthalpy, and occupancy rate change. Additionally, energy-saving potential was evaluated from the collected data through linear regression. The results show that when the outdoor air temperature, outdoor enthalpy, and occupancy rate are low, the parallel system is more energy-efficient due to the higher energy consumption of the chiller under low load conditions, which is suitable for winter. Conversely, the series system is more energy-efficient than under hot weather and high indoor load conditions, making it suitable for use in the summer.

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

Local exhaust characteristics of a cultural relic restoration workbench for different exhaust areas

The release of harmful gases and dust during the restoration of cultural relics has been a long-standing problem that cannot be ignored. A workbench is proposed for discharging pollutants during cultural relic restoration. The workbench adopts an exhaust air mode, which can reduce the area of the exhaust control plane and reduce the exhaust air volume of a semi-enclosed space through an air curtain at both ends. A theoretical analysis, experimental tests, and visual experiments are conducted to investigate the effectiveness of the proposed exhaust mode. The results indicated that the air curtain at both ends can effectively improve the exhaust-air velocity of the exhaust control plane. For an exhaust-air flow rate of 972 m3/h, increasing the air-supply velocity at the slot vents from 0 to 4.86 m/s causes the average exhaust-air velocity to increase from approximately 0.24 m/s to 0.34 m/s. Combining an exhaust-air flow rate of 766 m3/h with an air-supply velocity of 3.61 m/s requires a 37% lower exhaust-air flow rate per unit pollutant production rate than that required using pure exhaust air. The exhaust mode of the cultural relic restoration workbench has enormous potential for energy savings in ventilation.

Investigating the thermal performance of a pipe-encapsulated movable PCM wall and its impact on reducing indoor heat gain during summer

The utilization of phase change materials (PCM) in building envelopes is considered to be an effective technology for reducing energy consumption during building operations. In this study, a novel approach is presented for integrating PCM into walls by encapsulating it using pipes, allowing for its mobility through the use of compressed air. Four experimentally validated thermal models, pipe-encapsulated movable PCM (PEM-PCM) wall (Model 1), wall with PCM fixed to the exterior (Model 2), wall with PCM fixed to the interior (Model 3), and wall without PCM (Model 4), were proposed to compare their energy efficiency in summer. Different upper threshold limits (Ttr,up) and lower threshold limits (Ttr,low) were employed to explore the impact of various threshold combinations control strategies on the thermal performance of the wall. The results show that the most effective operating scheme is Ttr,up of 27 °C and Ttr,low of 25 °C. In terms of long-term operation, cumulative summer indoor heat gain of Model 1 is 10.65 %, 10.85 % and 11.4% lower than that of Model 2,3 and 4. Notably, September demonstrates the greatest potential for energy savings, with Model 1 reducing the monthly cumulative indoor heat gain by 54.12% compared to Model 4.

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

Architectural coatings and temperature Adaptive radiative cooling strategies applied in ten climate zones in China: Annual energy consumption Reductions and potentials

This research evaluates the energy-saving potential of high-reflectivity building materials, including three common coating types—normal (absorptivity 0.8, emissivity 0.8), cooling (absorptivity 0.2, emissivity 0.9), supercooling (absorptivity 0.05, emissivity 0.95), and Temperature-Adaptive Radiative Cooling (TARC) materials across 49 representative cities within ten climate zones in China using computational fluid dynamics simulations combined with the Demand.ninja energy consumption model. The results reveal that optimal solar absorptivity varies significantly between different climate zones, with higher absorptivity benefiting colder regions and lower absorptivity benefiting hotter regions. Urban microclimate simulations confirm that low solar absorption materials effectively reduce wall temperatures during the daytime and boost overall cooling energy savings, despite a slight increase in ambient air temperature. Building height-to-width ratios also influenced energy efficiency, with the best performance observed at a ratio of unity for both cooling and supercooling materials. Additionally, tests on the Temperature-Adaptive Radiative Cooling (TARC) materials demonstrate that TARCs offer significant energy-saving advantages, achieving an annual saving of 23.1 billion kWh across China by effectively balancing heating and cooling demands across different seasons. This superior performance is primarily attributed to TARCs’ ability to mitigate increased heating energy demands often associated with conventional cooling materials, especially in regions with substantial winter heating needs. Present research has highlighted the importance of climate-specific building material strategies and the potential of innovative materials like TARC for reducing energy consumptions, and this could favour the low carbon urban development.

Data-driven optimization reveals the impact of Urban Heat Island effect on the retrofit potential of building envelopes

The urban heat island (UHI) effect significantly impacts building energy consumption, but its effect on energy retrofit potential remains unclear. Here, we propose a data-driven surrogate optimization framework to assess the energy-saving potential of improving building envelope thermal performance in 101 public buildings in Shenzhen. The framework integrates batch building energy modeling, ensemble learning surrogate model training, and multi-objective optimization. The potential changes in building loads resulting from different parameter combinations are defined as energy-saving potential. We use the Urban Weather Generator to adjust typical meteorological year data and account for UHI effects in energy modeling. The results indicate that neglecting the UHI effect in Shenzhen overestimates the potential for building energy retrofitting by approximately 25.2 % (office buildings: -8.96 %, commercial buildings: 60.7 %). Among the nine thermal retrofitting parameters, the heat transfer coefficient of windows contributes the most to the energy-saving potential. Considering the UHI effect or not leads to significant differences in retrofitting strategies and optimal retrofitting parameter configurations. These findings underscore the importance of considering the UHI effect and interactions between buildings in energy retrofit modeling and decision-making processes to formulate precise retrofit strategies and schemes.

Building Information Modeling (BIM) for Energy Optimization in Green Buildings: A Case Study of a Residential Villa in the UAE

This paper explores the application of Building Information Modeling (BIM) in evaluating and optimizing energy efficiency in green buildings, focusing on a residential villa in Sharjah, UAE. The study assesses thermal bridges, energy consumption, and the potential energy savings from applying insulation. The villa’s energy performance was modeled using Revit and IES-VE software, and actual energy consumption was measured for comparison. The results show that applying insulation could reduce the villa's energy consumption by up to 17.91 MWh annually, confirming the accuracy of BIM in simulating energy performance. Additionally, this paper discusses how improvements in insulation and thermal management can significantly reduce the energy demands of residential buildings in hot climates.

Research on Energy Efficiency Optimization Control Strategy of Office Space Based on Genetic Simulated Annealing Strategy

Current energy-saving lighting control algorithms often face the dilemma of local optimality, which limits the energy-saving potential and comfort improvement of indoor lighting systems. The control parameters of the lighting system are optimized using a genetic simulated annealing algorithm to achieve the global optimal solution and enhance energy-saving efficacy in indoor lighting. The local search ability of the algorithm is enhanced by simulated annealing processing of excellent individuals after genetic operation. The genetic probability is adaptively adjusted according to the number of iterations and the fitness of the population, so that the algorithm enriches the population diversity in the early stage and avoids the “premature” convergence of the algorithm. A lamp illuminance model based on an artificial neural network and an indoor natural illuminance model based on a workbench are proposed to evaluate the lighting comfort, which provides a basis for constructing the fitness function of the optimization algorithm. Through the simulation experiment, the genetic simulated annealing algorithm is applied to the lighting scene introduced in this paper and compared with the traditional particle swarm optimization algorithm and genetic algorithm, the lighting energy saving performance is significantly improved.

UAV-BIM-BEM: An automatic unmanned aerial vehicles-based building energy model generation platform

Many existing and aging buildings were in urgent need of upgrades and renovations, but lack accurate geometric and energy consumption information. This study introduced an unmanned aerial vehicles-based (UAV-based) automated platform for building information modellings (BIM) to generate builidng energy modellings (BEM), which was called the UAV-BIM-BEM platform. The platform enabled the creation of energy models for existing buildings to estimate energy consumption and perform retrofit analysis. A four-story office building in Hong Kong, with dimensions of 52.3 m in length, 14.3 m in width, and 16.5 m in height, was selected as the case study building. Firstly, using UAV oblique photography techniques and path planning algorithms, the building images were transformed into a detailed 3D point cloud model. The point cloud model was provided to Revit to generate the BIM model in IFC format. The IFC file containing the geometric information of the building was then supplied to the AutoBPS-BIM tool, resulting in the generation of the BEM model in EnergyPlus to simulate detailed end-use energy consumption. The annual electricity use intensity (EUI) of the building was estimated to be 145.05 kWh/m2. Lastly, the generated energy model was used to evaluate the energy-saving potential of three different measures. The results showed that the electrical energy savings from cooling equipment, lighting systems, and window replacement are 17.22 %, 6.91 %, and 6.61 %, respectively. This research demonstrated that the UAV-BIM-BEM platform has great potential in BEM modeling for existing buildings and building energy retrofitting.

Phase change foam with temperature-adaptive radiative cooling feature for all-day building energy saving

Radiation cooling is an emerging energy-saving technology that requires a minimal energy input to achieve passive cooling. To date, the majority of research has focused on maximizing the cooling power. However, a powerful cooling performance may lead to overcooling at night or during cold seasons. In this study, we developed a phase change radiation cooling material (PCRCM) with temperature-adaptive cooling features to meet dynamic cooling requirements. Polydimethylsiloxane was selected as the flexible thermal emission substrate, while boron nitride nanosheets were chosen as the optical fillers. Consequently, radiation cooling materials exhibiting 97.5% reflectance and 94% emissivity were achieved. The phase change material (PCM) was then loaded onto the foam as an optical and thermal buffering medium. The obtained PCRCM exhibited 96% reflectance in hot atmosphere and 82.6% reflectance in cold atmosphere, passively cooling through thermal emission and PCM endotherms during the daytime, and actively heating through PCM exotherms at nighttime. Simulation and outdoor tests demonstrated that the PCRCM shows an excellent building energy-saving potential in different seasons.

Energy Saving for Impinging Jet Ventilation System by Employing Various Supply Duct Locations and Return Grill Elevation

The study of energy savings in ventilation systems within buildings is crucial. Impinging jet ventilation (IJV) systems have garnered significant interest from researchers. The identification of the appropriate location for the IJV reveals a gap in the existing literature. This research was conducted to address the existing gap by examining the impact of IJV location on energy savings and thermal comfort. A comprehensive three-dimensional CFD model is examined to accurately simulate the real environment of an office room (3 × 3 × 2.9 m3) during cooling mode, without the application of symmetrical plans. Four locations have been selected: two at the corners and two along the midwalls, designated for fixed-person positions. The return vent height is analyzed utilizing seven measurements: 2.9, 2.6, 2.3, 1.7, 1.1, 0.8, and 0.5 m. The RNG k–ε turbulence model is implemented alongside enhanced wall treatment. The findings indicated that the optimal range for the return vent height is between 1.7 and 0.8 m. It is advisable to utilize the IJV midwall 1 location, positioned behind the seated individual and away from the exterior hot wall. It is characterized by low vortex formation in the local working zone that contributes to a more comfortable sensation while providing recognized energy-saving potential.

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.