Enhance Energy Efficiency Research Articles

Enhancing energy efficiency in developing country households: Policy evaluation for household appliances in Ghana

Enhancing energy efficiency in developing country households: Policy evaluation for household appliances in Ghana

Design and Performance Analysis of Photovoltaic Solar Cells Using WSe2 as an Absorber Layer with SnS2 Electron Transport Layer

Recent breakthroughs in solar cell technology have highlighted transition metal dichalcogenides, particularly tungsten diselenide (WSe₂), as exceptional absorber materials due to their remarkable optoelectronic properties. This study presents an innovative thin-film photovoltaic solar cell featuring Cu₂O-WSe₂-SnS₂ layers. Utilizing WSe₂ as the primary absorber, SnS₂ as the electron transport layer (ETL), and Cu₂O as the hole transport layer (HTL), this structure is engineered to maximize light absorption and carrier separation, enhancing energy efficiency. Key performance parameters, including power conversion efficiency (PCE), fill factor (FF), short-circuit current density (Jsc), and open-circuit voltage (Voc), were thoroughly evaluated. The impressive results—PCE of 25.76%, FF of 83.36%, Voc of 1.29 V, and Jsc of 23.84 mA/cm²—were achieved through meticulous simulation and experimental validation. Investigating defect densities at the SnS₂/WSe₂ and WSe₂/Cu₂O interfaces revealed that minimizing interfacial recombination significantly enhances charge extraction and overall performance. A comparative analysis confirmed SnS₂ as an optimal ETL due to superior electron mobility and minimal recombination. This optimized structure offers excellent efficiency and operational stability, providing crucial insights into the feasibility of WSe₂-based thin-film solar cells. Additionally, it advances our understanding of interfacial engineering in photovoltaics and underscores the role of WSe₂ in conjunction with Cu₂O and SnS₂. These findings contribute to ongoing research on high-efficiency thin-film solar cells, paving the way for further innovations in solar energy conversion technology.

Examining the Environmental Impact of High-Powered Computing

In recent years, the popularity of high-performance computing (HPC) has made its environmental impact increasingly prominent. The operation of HPC equipment generates ecological problems such as air pollution, water wastage, and solid waste pollution, posing a serious threat to ecosystems and human health. This study aims to develop a broad model to assess the environmental impacts of carbon emissions, cooling water resources required, and e-waste generated during HPC operation. The ecological effects of HPC in terms of atmosphere, water resources, and solid waste were assessed through the model. We suggest technical and policy solutions designed to enhance energy efficiency, boost the share of renewable energy sources, refine the placement of data centers, encourage sustainable supply chains, and strengthen e-waste recycling programs. It highlights the criticality of the environmental impact of HPC and presents our research insights and recommendations to support global efforts to achieve sustainable development.

Numerical Analysis of Phase-Change Material Integration in Building Envelopes: A Case Study in Lebanon

The building sector is a major global energy consumer, particularly in Lebanon, where heating and air conditioning demand remains high. Integrating Phase Change Material (PCM) into building envelopes presents a promising solution for latent heat storage and enhanced energy efficiency. This study investigates the optimal wall configurations for improved thermal performance using PCM in two Lebanese regions: Beirut and Bekaa. Using ANSYS Fluent, various wall configurations were analyzed to determine the most effective placement of PCM. The optimal configurations were then evaluated in DesignBuilder to estimate energy savings. Results indicate that in Bekaa, external PCM and insulation provide the best performance due to strong dependence on external conditions, whereas in Beirut, internal PCM and insulation are more effective. PCM implementation in both regions significantly reduces energy consumption, with Bekaa proving more advantageous as it does not require additional cooling mechanisms. This research underscores the potential of PCM as a viable strategy for enhancing energy efficiency in building envelopes, with relevance to the climatic conditions of Beirut and Bekaa.

Multi-Objective Optimization of Envelope Structures for Rural Dwellings in Qianbei Region, China: Synergistic Enhancement of Energy Efficiency, Thermal Comfort, and Economic Viability

In China, retrofitting rural dwellings is a crucial step toward enhancing living conditions and lowering energy waste. One of the most important ways to enhance building performance is to retrofit the building envelope. The Qianbei Region’s (Northern Guizhou Province, China) rural dwellings are the subject of this study. It identifies the persistent issue of inadequate thermal comfort in local rural dwellings through indoor thermal environment measurements and questionnaire surveys. Using a parametric modelling tool (Rhino-Grasshopper-Ladybug Tools), multi-objective optimization was performed using a non-dominated sorting genetic algorithm (NSGA-II), with the types of external windows, walls, and roof insulation as optimization variables, and building energy consumption (E), annual thermal discomfort hours (TDT), and life cycle cost increment (ΔLCC) as optimization objectives. After the retrofitting, the building’s energy consumption was reduced from the baseline value of 96.41 kWh/m2 to 42.40 kWh/m2 (a 56% reduction), and the annual duration of thermal discomfort decreased from 6173 h to 5078 h (a 17.7% decrease). This resulted in a positive economic return, with a cost saving of ΔLCC = −56,329.87 CNY. The research proposes a scientific method for the energy-saving retrofitting of rural dwellings in the Qianbei Region, which also serves as a guide for the optimization of building performance in comparable climate zones.

Optimizing Task Offloading and Resource Management in 6G Networks Through a Hierarchical Edge‐Fog‐Cloud Architecture

ABSTRACTThe high‐speed development of 6G technology demands novel network architectures that can manage increasing volumes of data and connected devices efficiently. This paper introduces a novel hierarchical edge‐fog‐cloud (HEFC) architecture in resource allocation and energy efficiency optimized for 6G networks. The next generation of communication should be delivered by 6G technology that will make the design of interaction between humans, data, and devices. Because of requirements in 6G, HEFC architecture dynamically allocates computational tasks to HEFC depending on complexity latency requirements. The edge layer is responsible for most latency‐sensitive tasks. However, the current usage of edge and fog computing models takes into account only the single‐point optimization. The proposed HEFC model achieves a low latency of 8 ms, an energy consumption of 110 kWh, a high throughput of 600 tasks/s, and an efficient resource utilization of 85%. The main aim of our study is to design a solid, robust and scalable HEFC architecture that can tackle the challenges of task offloading, resource allocation and energy consumption for the 6G systems. The approach employs dynamic voltage and frequency scaling (DVFS) with advanced optimization methods to enhance energy efficiency and scalability in the network core. By efficiently allocating workloads, the proposed EFC model excels existing architectures in important performance areas. This paper presents sustainable 6G network model to tackle the challenge of latency‐sensitive and energy‐greedy applications. The results can guide future network designs towards more efficient and adaptive architectures, making 6G networks more responsive and dependable to evolving technological demands.

Analyzing the impact of remittance and electricity consumption on ecological footprint in South Asia: the role of globalization using panel ARDL and MMQR method

Addressing the ecological footprint is crucial for achieving sustainable development in South Asia, where the rapid economic and social developments present substantial challenges to the environment. This study aims to examine the effects of remittances, urbanization and electricity consumption on South Asia's ecological footprint, with a focus on the role of globalization. By employing a Panel ARDL framework on data spanning 2000 to 2021, we investigate the long-term and short-term dynamics of the variables. Additionally, we implement the Method of Moments Quantile Regression (MMQR) technique, which illustrated the independent variables' specific effects on the ecological footprint at various quantiles. Before utilizing these regression techniques, we conduct a set of diagnostic tests such as Cross‐Sectional Dependence Test, Unit root tests (LLC, IPS, CIPS, CADF) and panel cointegration test. According to the findings of our study, remittances have a reducing effect on the ecological footprint, while GDP per capita and urbanization considerably increase it. Electricity consumption has a smaller but still significant impact on environmental degradation. Globalization exhibits an intricate correlation, encompassing both advantageous and detrimental impacts on the ecological footprint. The Dumitrescu-Hurlin causality test's findings revealed a unidirectional causality from remittances and urbanization to the ecological footprint and bidirectional causality between electricity consumption and the ecological footprint. The study proposes implementing policy measures that encourage green economic strategies, prioritize the promotion of sustainable urban practices, enhancement of energy efficiency, utilization of the advantages of globalization for the environment, and efficient management of remittances to reduce ecological strain.

Effects of Harmless Municipal Solid Waste Incineration Fly Ash on the Macroscopic Properties and Microstructure of Recycled Aggregate Concrete

With the increasing rate of urbanization and the annual rise in municipal domestic waste, the use of harmless municipal solid waste incineration fly ash (HMSWIFA) as a construction material has been gradually adopted and promoted. However, significant differences exist in how various characteristics of HMSWIFA affect the performance of recycled aggregate concrete (RCA). To analyze the effects of HMSWIFA content and particle size on the macroscopic properties and microstructure of RCA, this paper conducts compressive, flexural, frost resistance, and Scanning Electron Microscope (SEM) characterization on RCA with varying dosages and particle sizes of HMSWIFA as a cement replacement. The results indicate that HMSWIFA enhances the compressive strength (CS) and frost resistance of RCA. Experimental data reveal that HMSWIFA with a particle size of 600–900 μm exhibits the best modification effect at an admixture level of 10–15%: the 28-day CS increased by 1.90–3.60%, the mass loss after freezing and thawing decreased by 0.37–0.45%, and the increase in dynamic elastic modulus reached 16.09–16.44%. Notably, the flexural strength (FS) experienced a reduction of 1.81% at a high dosage of the optimal particle size. This study elucidates the coupling relationship of “particle size-admixture-performance” in HMSWIFA-modified recycled concrete, demonstrating that reasonable control of the particle size distribution of HMSWIFA can achieve a synergistic effect of mechanical enhancement and durability improvement. The research findings provide a valuable reference for the application of municipal waste incineration HMSWIFA in RCA, facilitating the recycling of waste resources to mitigate pollution and enhance energy efficiency.

Designing and Implementing Aluminum Composite Panels (ACPs) for Sustainable and Safe Urban Buildings in the United States: The National Importance and Impact on Modern Architecture

As urbanization accelerates across the United States, cities face mounting challenges, including aging infrastructure, environmental degradation, and the need for sustainable and resilient development. The integration of Aluminum Composite Panels (ACPs) serves as a critical component in modern urban architecture to enhance sustainability, energy efficiency, and structural resilience. ACPs offer numerous advantages, including lightweight construction, durability, fire resistance, and design flexibility, making them valuable material for urban infrastructure. By examining case studies, urban planning strategies, and technological tools such as GIS, ArcGIS Pro, and AutoCAD, this research shows how ACPs can contribute to cost-effective, environmentally friendly, and aesthetically appealing urban spaces. The study also assesses the economic and environmental impacts of ACP integration, providing policy recommendations for widespread adoption. Through innovative material selection and strategic urban planning, ACPs have the potential to redefine the architectural landscape of U.S. cities, promoting a more sustainable and resilient built environment.  Background Information Urban centers in the U.S. face complex challenges, including housing shortages, aging infrastructure, and growing populations, alongside environmental concerns such as energy efficiency, carbon emissions, and the urgent need for climate resilience. Sustainable urban development has emerged as a critical framework, emphasizing livable, environmentally responsible, and economically viable cities. However, Innovative building materials like ACPs play a crucial role, offering enhanced durability, energy efficiency, and aesthetic appeal. This project aims to explore how ACPs contribute to these goals, providing a comprehensive overview of their potential.  Methods and Approach The research employs a comprehensive literature review to examine the interconnected themes of urban challenges, sustainable urban development, and innovative building materials. It analyzes the properties and applications of ACPs in modern architecture, focusing on their role in enhancing durability, energy efficiency, and aesthetic quality. Furthermore, the study explores urban planning strategies for enhancing infrastructure resilience, considering innovative materials and effective integration techniques.  Key Findings and Results ACPs have evolved from simple signage materials to integral components in building facades, offering a unique blend of aesthetic versatility, durability, and functional performance. They enhance energy efficiency by providing excellent insulation properties, reducing overall energy consumption in buildings. Furthermore, fire-resistant ACP variants align with national and international safety standards, addressing stringent building codes and fire safety concerns. The research also highlights the transformative capabilities of GIS, ArcGIS Pro, and AutoCAD in optimizing land use, fostering sustainability, and enhancing the functional quality of urban structures.

Dynamic vertical green walls & shading systems integration to enhance energy efficiency & thermal comfort: A case of educational building skin retrofit in a Mediterranean city

Dynamic vertical green walls & shading systems integration to enhance energy efficiency & thermal comfort: A case of educational building skin retrofit in a Mediterranean city

Enhancing decentralized energy storage investments with artificial intelligence-driven decision models

Decentralized energy storage investments play a crucial role in enhancing energy efficiency and promoting renewable energy integration. However, the complexity of these projects and the limited resources of the companies make it necessary to determine strategic priorities. This paper tries to define effective investment strategies for the improvements of the decentralized energy storage projects. In the first stage, the selection of mass experts is made via information gain-based mass expert selection. Next, the assessments of the experts are balanced based on the opinion of the best expert by using q-learning algorithm. Moreover, determinants of decentralized energy storage investments are examined with molecular fuzzy (MF) cognitive maps. Finally, strategy alternatives for decentralized energy storage investments are ranked with MF multi-objective particle swarm optimization (MOPSO). The main contribution of this study is the identification of the most effective decentralized energy storage investment alternatives by establishing a novel model. The main novelty of the proposed model is that considering information gain-based mass expert selection technique allows for higher consistency and decision efficiency. Owing to this issue, the decision-making process is accelerated, and the applicability of the results increases. The findings indicate that customer expectations (weight: 0.2577) and financial issues (weight: 0.2513) are the most essential criteria in improving the performance of decentralized energy storage investments. Furthermore, hydrogen-based energy storage (average value: 0.1878) and distributed battery swapping stations (average value: 0.1877) are the most important decentralized energy storage investment alternatives.

Study of a hybrid heating system combining air source heat pump and solar energy with a pebble heat storage bed

ABSTRACT Solar energy and air source heat pumps are both recognized for their environmentally friendly and energy-efficient characteristics. This study introduces an innovative hybrid heating system that integrates a solar hot air system with a pebble heat storage bed and a low-temperature air source heat pump, designed for severe cold regions. Utilizing TRNSYS software, we investigated the heating performance and duration of this system in extreme cold climates and compared it with a standalone low-temperature air source system. Our findings reveal that the solar air collector side of the hybrid system contributed 950.4 h to room heating over the entire heating season, representing 26.2% of the total heating season and 66.1% of the system’s operational time. The hybrid system demonstrated a significant reduction in total energy consumption by 386.54kWh and a 23.8% improvement in the coefficient of performance (COP) compared to the low-temperature air source system alone. This research offers a novel approach to heating solutions for severe cold regions, enhancing energy efficiency and sustainability.

Research on Optimization Design of Cold Resistant New Energy Vehicle Battery Pack Structure Based on Quantum Computing

Cold climates reduce battery efficiency in new energy vehicles (NEVs), necessitating optimized thermal-resistant designs leveraging advanced quantum and classical optimization techniques. Existing approaches struggle with optimizing complex, multi-objective battery pack designs for cold climates, often lacking efficiency, scalability, and integration of advanced computational techniques like quantum optimization for improved results. To develop an advanced optimization framework for designing cold-resistant battery packs that enhance energy efficiency, thermal stability, and structural durability under extreme cold conditions, ensuring improved performance and reliability for NEVs operating in challenging climates. The proposed cold-resistant new energy vehicle battery pack structure (CRNEV-BPS) framework optimizes cold-resistant battery pack design through initial parameter setup, and hybrid optimization using quantum and Bayesian methods to enhance energy efficiency and thermal stability. In the parameter setup and design for the cold-resistant battery pack, lithium iron phosphate (LiFePO4) is used for stable cell performance, graphite for heat dissipation, aluminum alloys for a lightweight structure, silicon-based semiconductors for efficient power management, and copper for electrical conductivity. The quantum approximate mutated Bayesian algorithm (QAM-BO) combines quantum computing’s approximation power with Bayesian optimization and mutation techniques to enhance the search for optimal solutions in multifaceted design spaces, improving accuracy (95%). This effectively searches the design spaces that have well-refined battery pack parameters for optimal performance in cold weather. CRNEV-BPS achieves improved energy efficiency, thermal stability, and structural integrity for NEVs in extreme climates.

A review of multi objective optimization in sustainable architecture: enhancing energy efficiency through dynamic facades

A review of multi objective optimization in sustainable architecture: enhancing energy efficiency through dynamic facades

Analysis of Green Total Factor Energy Efficiency in OECD Countries Based on a Super-efficiency SBM-DEA Model

In the context of intensifying climate change and increasing resource constraints, addressing the dual challenges of economic growth and environmental sustainability has emerged as a critical global priority. Green Total Factor Energy Efficiency (GTFEE), an essential metric for evaluating the coordination between economic development, energy efficiency, and environmental protection, plays a pivotal role in optimizing resource allocation, fostering technological innovation, and supporting the pursuit of sustainable development. However, existing research has yet to provide a comprehensive and systematic analysis of GTFEE’s long-term trends and its underlying driving mechanisms. This study addresses this gap by focusing on OECD (Organization for Economic Co-operation and Development) countries, employing the super-efficient SBM-DEA model with non-expected outputs to evaluate GTFEE from 1995 to 2021 systematically. The analysis delves into the spatial and temporal characteristics of GTFEE and its dynamic evolution patterns. The findings reveal a sustained upward trajectory in GTFEE across OECD countries, with the average score rising from 0.7814 in 1995 to 0.8894 in 2021. Nonetheless, substantial heterogeneity persists in GTFEE levels among regions and countries. Further, using quantile random forest regression analysis, the study identifies critical determinants of GTFEE, including economic development levels, energy intensity, technological innovation capacity, industrial structure optimization, fiscal revenue, and urbanization. These results not only elucidate the driving mechanisms of GTFEE but also offer a robust theoretical foundation and actionable policy insights for advancing green energy transitions and achieving sustainable development goals. A comprehensive and integrated assessment of Green Total Factor Energy Efficiency (GTFEE) in OECD countries is crucial for advancing sustainable development. This study systematically measures GTFEE in OECD countries over the period 1995-2021, utilizing the super-efficient SBM-DEA model with an undesired output. It further analyzes the spatial and temporal distribution characteristics and dynamic evolution of GTFEE. The results indicate that, overall, GTFEE in OECD countries exhibits an upward trend throughout the study period, with the average value increasing from 0.7814 in 1995 to 0.8894 in 2021, reflecting improvements in green total factor energy efficiency. However, substantial disparities in GTFEE levels are observed across countries, suggesting varied rates of progress and effectiveness in promoting green transitions and enhancing energy efficiency. Through quantile random forest regression analysis, the study identifies key determinants influencing GTFEE, including the level of economic development (as measured by GNI per capita), energy intensity (primary energy consumption), technological innovation capacity, industrial structure optimization, fiscal revenue, and urbanization.

Control Strategy for Current Stress Mitigation in Dual Active Bridge for Electrical Vehicle Application

ABSTRACTThis paper presents a novel control strategy for the isolated dual active bridge bidirectional DC‐DC converter (IBDC), aimed at addressing the challenges of current stress in conventional phase‐shift control methods. The proposed modified dual phase‐shift (DPS) technique incorporates an innovative switching approach, effectively reducing current stress, minimizing power losses, and enhancing overall efficiency. Theoretical modeling, simulations, and practical experiments validate the proposed technique, demonstrating its ability to achieve soft switching and expand the zero voltage switching (ZVS) region. Experimental results from a 2.4‐kW DAB prototype further confirm the effectiveness of the proposed control strategy. This research contributes to advancing the efficiency and performance of IBDCs, particularly in electric vehicles, by enabling enhanced energy efficiency, extended range, and reduced operational costs, thereby supporting the development of sustainable transportation and robust power electronics systems.

A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings

Improving energy efficiency is a major concern in residential buildings for economic prosperity and environmental stability. Despite growing interest in this area, limited research has been conducted to systematically identify the primary factors that influence residential energy efficiency at scale, leaving a significant research gap. This paper addresses the gap by exploring the key determinant factors of energy efficiency in residential properties using a large-scale energy performance certificate dataset. Dimensionality reduction and feature selection techniques were used to pinpoint the key predictors of energy efficiency. The consistent results emphasise the importance of CO2 emissions per floor area, current energy consumption, heating cost current, and CO2 emissions current as primary determinants, alongside factors such as total floor area, lighting cost, and heated rooms. Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. These findings provide valuable data-driven insights for stakeholders seeking to enhance energy efficiency in residential buildings. Additionally, a customised machine learning interface was developed to visualise the multifaceted data analyses and model evaluations, promoting informed decision-making.

Adaptive Load-Balanced Clustering for Enhanced Energy Efficiency and Fault Tolerance in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are critical in distributed monitoring systems, where optimal performance relies on efficient energy usage, balanced data handling, and fault resilience. Traditional clustering protocols, such as Energy-Aware Hybrid Clustering (EAHC), primarily focus on energy metrics but often neglect real-time load balancing and adaptive reconfiguration. This leads to node failures, uneven energy depletion, and increased latency. Clustering methods lacking dynamic adaptation to node load and failure conditions often face performance degradation due to hotspot formation, unbalanced load, and delayed reconfiguration. These issues negatively affect network lifetime, throughput, and data latency, particularly in large-scale and heterogeneous WSNs. The proposed Adaptive Load-Balanced Clustering (ALBC) model addresses these limitations by dynamically forming clusters based on real-time metrics such as node load, energy levels, data rates, and fault tolerance. A mathematical framework involving energy and data rate constraints, load variance minimization, and reconfiguration cost is developed. The cluster head (CH) selection process favors nodes with optimal energy-to-load ratios while ensuring connectivity and minimal latency. The model is validated using MATLAB simulations against four existing models: EAHC, LEACH, HEED, and EEHC. Simulation results show that ALBC significantly reduces energy consumption and latency while enhancing load balance and fault tolerance. It achieves up to 22% lower load variance, 18% higher throughput, and 30% fewer reconfigurations compared to EAHC. The model adapts seamlessly to node failures, ensuring uninterrupted data flow and prolonged network lifespan.

Managing China's Energy Efficiency

Managing energy efficiency is essential for ensuring national energy security and facilitating a transition to a green economy. Outer circulation creates favorable external conditions. Using data from listed firms information in China between 2007 and 2016, this paper examines the effect of cross-border mergers and acquisitions (M&A) on energy efficiency through a PSM-DID model. The research shows that: (1) M&A can effectively manage and improve energy efficiency of domestic enterprises, with the positive impact showing a clear trend of increasing annually. (2) The mechanism analysis suggests that the enhancement of energy efficiency is driven by technological and structural effects. (3) Furthermore, the rise in energy efficiency benefits more from M&A in regions along the Belt and Road Initiative and countries with stricter environmental regulations. The findings of this paper provide important insights into the management of international capital flows, energy efficiency improvement, and sustainable development.

Overview and Future Trends of Intelligent Technology Applications of Smart Ports in China

Ports play a crucial role in sustaining human society. In recent years, the rapid development of China's ports, along with the swift growth in container throughput, has brought significant pressure on port management. As a result, smart ports have garnered widespread attention from scholars and practitioners. This study evaluates the current state of smart port research using scientometric methods, analyzing literature from the Web of Science Core Collection. The focus is on balancing technology, environmental sustainability, and economic development. Key findings reveal that current research trends emphasize integrating advanced technologies like Internet of Things (IoT) and Digital Twin (DT), enhancing energy efficiency, and driving digital transformation in port operations. The study identifies three main research directions: the application of Information and Communication Technology (ICT), environmental sustainability, and safety in multimodal transport systems. It also highlights the evolution of research themes from 2001-2015focusing on Infrastructure upgrading. 2016-2020 focuses on big data and pollution. 2021-2024 with an emphasis on smart ports and sustainability. The research direction of smart ports trends to evolve and gradually adapt to the needs of global technological innovation, multimodal logistics, management and sustainable development. The paper provides insights into key thematic categories, influential authors and journals, and emerging trends, offering valuable guidance for new researchers in the smart port field.