Increase Energy Efficiency Research Articles

High Voltage Design for Quasi-Solid Zinc-Air Batteries.

Alkaline zinc-air batteries (ZABs) are recognized as a promising candidate for next generation of safe battery systems, owing to their inherent advantages. However, the performance of traditional ZABs is constrained by a lower theoretical voltage of 1.65 V, presenting a significant challenge in enhancing their practical application. This paper demonstrates a prototype of acid-alkaline hybrid quasi-solid-state zinc-air battery (HSZAB), featuring a unique design in both a new acidic gel electrolyte and battery structure. Our approach involves regulating the electrolyte pH and ionic conductivity to achieve an enhanced theoretical voltage, resulting in an open circuit voltage of up to 2.0 V. Additionally, the HSZAB demonstrates substantially improved peak power density (417 mW cm-², five times higher than the conventional alkaline ZABs) and increased energy efficiency (from 60% to 82%). This finding underscores the promising prospects of high voltage zinc-air batteries, offering a substantial step forward in the field of energy storage systems.

MODELING OF ENERGY CONSUMPTION INDICATORS OF AN APARTMENT BUILDING USING SPECIALIZED SOFTWARE PRODUCTS

Increasing the energy efficiency of the building stock is a strategic task for Ukraine, and to attract funding for comprehensive thermal modernization projects, it is necessary to conduct an energy audit and draw up an energy certificate for residential and multi-apartment buildings. Energy auditors in Ukraine usually develop their own models for calculations, but there are software products that allow modeling the energy consumption of buildings taking into account the features of operation, characteristics of enclosures and engineering systems. Studying their capabilities and comparative analysis is a relevant task. The use of software products by energy auditors helps automate energy consumption calculations before and after the implementation of energy saving measures, develop an energy certificate, and accelerate the implementation of complex thermal modernization projects. The purpose of the work is to review the capabilities of software products for calculating specific energy consumption for various needs and energy efficiency classes, as well as developing an energy certificate with a comparative analysis using the example of a residential multi-apartment building. Research methods: analytical methods, comparative and structural analysis method, feasibility studies using E-Audit, Audytor OZC, MS Excel, etc. In accordance with the purpose of the work, the following tasks were set: - to study the capabilities of software products for modeling energy characteristics; - to conduct an energy survey of the building; - to develop a mathematical model for calculating specific energy consumption and energy efficiency class according to the national calculation methodology; - to calculate energy consumption using specialized software products with the development of an energy certificate; - to justify measures to increase energy efficiency for the building with the determination of energy and economic effects.

REVIEW OF SUPPORT PROGRAMS FOR COMPREHENSIVE PROJECTS TO IMPROVE ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS AND ANALYSIS OF THE RESULTS OF THEIR IMPLEMENTATION

In modern conditions, the issue of energy efficiency in the residential sector is becoming increasingly relevant, as apartment buildings consume a significant amount of energy, and the cost of energy carriers is constantly growing. More than 70% of the housing stock was built during the period of mass construction, so the thermal characteristics of the enclosures do not meet modern energy efficiency requirements, and engineering networks are worn out and require modernization. Taking measures to increase energy efficiency in the residential sector will contribute to the energy independence of our state and reduce the impact on the environment. The country's recovery after the end of military operations must take place in compliance with energy efficiency requirements. The State of Ukraine and international financial organizations support energy efficiency measures in the housing and communal services by implementing various investment programs and grants. The implementation of such projects allows you to reduce energy costs and improve the comfort of living in residential buildings. The purpose of the study is to assess the performance of projects implementing comprehensive technical solutions for energy efficiency in the residential sector; analyze the conditions for participation in such projects and the results of implementing various support programs. The study reviewed the housing sector support programs successfully implemented in Ukraine and currently in operation, the instruments of state regulation of energy efficiency improvement processes in housing and communal services, and also analyzed in detail the technical and economic indicators of energy efficiency improvement projects of the Energodim program from the Energy Efficiency Fund. Research methods used: analytical methods, system analysis, synthesis, systematization, comparative and structural analysis method.

Challenges and opportunities for increase sustainability and energy efficiency in ceramic tile industry

ABSTRACTThe ceramic tile has an estimated global warming potential of 14.4 kg CO2 equivalent per square meter, with CO2 emissions contributing to approximately 92.1% of the overall impact. The total emissions amount to 19 million tons of CO2 annually, representing approximately 1% of Europe's industrial emissions that are regulated by the EU Emissions Trading System. As a result, the ceramic industry is under growing pressure to decrease carbon emissions and improve energy efficiency in accordance with Europe's new climate and energy objectives. However, this process holds a clear challenge. This work addresses the challenges and opportunities facing the ceramic tile industry in reducing carbon emissions and improving energy efficiency in line with new climate and energy goals worldwide. It highlights the importance of adopting innovative strategies across product, process efficiency, and market and supply chain aspects.

NiSNN-A: Noniterative Spiking Neural Network With Attention With Application to Motor Imagery EEG Classification.

Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning (DL) algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a noniterative leaky integrate-and-fire (NiLIF) neuron model, overcoming the gradient issues in traditional SNNs that use iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed noniterative SNN with attention (NiSNN-A) model on two MI EEG datasets, OpenBMI and BCIC IV 2a. Experimental results demonstrate that: 1) our model outperforms other SNN models by achieving higher accuracy and 2) our model increases energy efficiency compared with the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy.

Food waste minimisation and energy efficiency for carbon emission reduction.

This research determines the potential impact of reducing food waste on future energy consumption and pollutant emissions. The study uses system dynamics modelling to simulate the complex link between population, food demand, food waste output and their interactions with energy consumption in the food system and carbon dioxide (CO2) emissions. Scenarios are developed by considering two elements: a reduction in food waste and an increase in energy output. Based on a case study of Delhi, food demand and energy consumption are expected to rise by 6% and 35% every year, respectively, from 2023 to 2033. The model predicts that a 20% reduction in food waste, combined with a 20% increase in energy efficiency, could reduce CO2 emissions by 23.17% by 2033. The combination scenario proved to be the most efficient in reducing carbon emissions and energy consumption. This significant reduction in emissions highlights the potential of integrated food waste and energy management strategies in mitigating environmental impact.

Adaptive digital twin integration with multilevel inverter control for energy efficient smart rehabilitation systems

This research proposes an innovative framework integrating adaptive Digital Twin (DT) models with Multi-Level Inverter (MLI) control to improve energy efficiency in advanced rehabilitation systems. By utilizing real-time monitoring and adaptive adjustment of power parameters through DT technology, the method achieves precise and dynamic control of devices such as prosthetics and exoskeletons. The incorporation of MLI ensures smooth and efficient power delivery, reducing harmonic distortion and enhancing overall energy utilization. Key outcomes include a 14.05% increase in energy efficiency, an 8.12% decrease in power ripple, and a 24.03% improvement in system response accuracy, enabling real-time optimization tailored to patient-specific rehabilitation needs. Furthermore, the proposed approach lowers operational costs by 7.01% through optimized energy usage and extended system lifespan. These results highlight the potential of this innovative method to advance rehabilitation systems through the integration of adaptive control and real-time digital modeling.

Driving Energy Transition Through Artificial Intelligence: Integrating Economic, Environmental, Social, and Governance (ECON-ESG) Factors in OECD Countries

Abstract This research examines the impacts of economic (ECON) factors, ESG factors, and artificial intelligence (AI) factors on energy transition (ET) in 33 OECD countries. The CS-ARDL model is utilized to analyze both long-term links and short-term dynamics in panel datasets, accounting for cross-sectional dependence, between 2002 and 2020. The findings indicate that while economic factors (ECON) negatively affect energy transition (ET), environmental factors (E) positively affect it. However, while social factors and AI demonstrate negative relationships with ET, governance factors demonstrate positive relationships. Finally, the impact of ECON-ESG on energy transition (ET) is negative. Some inferences and policy implications are as follows: (i) The negative effects of economic factors (ECON) on ET suggest that focusing on energy transition through only economic growth cannot be enough. Economic growth should be integrated into the energy transition by increasing energy efficiency. (ii) The negative effect of artificial intelligence (AI) on ET can be interpreted as the high investment costs of AI technologies may make energy transition projects challenging to finance and reduce their applicability. Policymakers can develop innovative financing models to finance energy transition projects related to AI technologies. For example, they can establish performance-based financing models for energy efficiency services. (iii) The negative effect of broad-based sustainability (ECON-ESG) with additional economic factors (ECON) to ESG on ET can be interpreted as follows. Some economic policies may contradict environmental, social, and government policies. For example, policies encouraging short-term economic growth can damage long-term environmental and social impacts, negatively impacting the energy transition. Adopting a holistic approach that integrates economic, environmental, social, governance, and artificial intelligence factors is critical for policymakers to accelerate energy transition and achieve sustainable development goals.

Logistics and environmental aspects of the formation of mining and transport systems of dump formation in deep pits using container technology

Improving the energy efficiency and environmental friendliness of waste disposal processes during open-pit mining of mineral deposits involves substantiating and developing a new mining and transport technology. Literary analysis combined with computer modeling based on the finite element method has made it possible to automate the work in designing container technology equipment at the design preparation stages. A new mining and transport system for waste disposal in deep quarries has been developed, which involves the use of container technology for moving rock mass in quarries, allowing for increased energy efficiency of transport systems and minimizing pollution of the quarry atmosphere. The proposed logistic scheme for transporting rock mass to dumps using container technology ensures the creation of a new energy-saving mining and transport system using quarry hoisting machines, which, in comparison with similar traditional systems, allows for a reduction in costs for lifting rock mass, a reduction in atmospheric gas pollution, and an improvement in mining modes. Container delivery of rock mass as a bulk and difficult-to-excavate material will allow for its single excavation and lifting from the quarry to the dump using quarry hoisting machines with a minimum tare coefficient.

An Analysis of the Increase in Energy Efficiency of Photovoltaic Installations by Using Bifacial Modules

This work concerns the experimental verification of changes in the energy efficiency of photovoltaic installations through the use of bifacial modules. For this purpose, an experimental stand was designed and built for the comparative analysis of the efficiency of two types of photovoltaic panels: bifacial (bPV) and monofacial (mPV). The tests consisted of placing the panels at different heights above the ground surface and at different angles. During the tests, three substrates with different albedo were taken into account: green grass, gray concrete (fabric), and white snow (polystyrene). The tests for both types of panels were carried out simultaneously (in parallel), which guaranteed the same environmental conditions (temperature and solar radiation intensity). Based on the results of the voltage and current measurements for different angles of PV module inclination and, for bPV panels, different heights above the ground surface and different types of substrate, a series of current–voltage characteristics and power characteristics were plotted. The “additional” energy efficiency of bifacial panels compared to monofacial panels was also determined. It was shown that under favorable conditions, using bifacial panels instead of monofacial panels can increase the production of electricity by more than 56% from structures of the same dimensions. The research results can be of great value when designing photovoltaic installations.

Navigating unhinged paradoxes: an interdisciplinary roadmap for energy efficiency in data centers

ABSTRACT Interdisciplinary teams skilled in Aristotle’s three knowledge forms, phronesis, episteme, techne, are essential for dealing with complex challenges in future. As sectors transition to new concepts, risks exist for unhinged paradoxes, i.e., unforeseen consequences. This is particularly relevant for industries with complex infrastructures, such as data centers (DCs). This paper presents a conceptual roadmap consisting of four interconnected phases for integrating energy efficiency concepts in DCs, with a focus on avoiding unhinged paradoxes. The knowledge creation process during the roadmapping process underscores the importance from (1) the acknowledgement of an external threat, (2) inclusion of an interdisciplinary team, and (3) regular project meetings to facilitate a collaborative and interdisciplinary environment. Additionally, the insights gained from this research extend to sectors beyond DCs and can be applied to other industries working towards increased energy efficiency.

The Role of Poverty and Gender in Shaping Households’ Energy Consumption Patterns in Selected European Countries

In the context of Sustainable Development Goals, declining poverty (Goal 1), achieving gender equality (Goal 5), and ensuring access to clean and affordable energy (SDG7) are still behind track, and the gaps are not yet ready to be rapidly fulfilled. As part of Goal 7, energy consumption-related targets still lack the potential to be targeted. Considering these elements, this study aims to determine the impact of poverty and gender equality on the energy consumption of households in several European countries. Using data from 2010 to 2022 and the moments’ quantile regression method combined with a pooled OLS based on Driskoll-Kraay estimators, we found statistically significant results regarding the impact of poverty and gender on household energy consumption. These findings’ significance will direct policy design towards those meaningful tools that will increase energy efficiency, address energy poverty, and ensure energy just transition, leaving no one behind. Based on the main findings, the policymakers can understand that a mix of policies is significantly more efficient. In such circumstances, social and economic inequalities will not successfully address development issues without including key environmental priorities, such as emissions mitigation and energy consumption patterns.

Reducing Atmospheric Pollution as the Basis of a Regional Circular Economy: Evidence from Kazakhstan

Reducing atmospheric emissions through the introduction of circular economy principles is one of the current tasks of sustainable regional development. The purpose of this research is to study the impact of the actions taken by Karachaganak Petroleum Operating B.V. (KPO) to reduce air pollution, and, based on this, to evaluate the potential of forming a circular economy in one of the biggest regions of Kazakhstan in which KPO is operating. The air pollution in the region is related to the oil and gas production activities of the company. This study was conducted using econometric modeling and statistical and comparative analyses. This study’s value lies in its interdisciplinary approach, which made it possible to combine environmental and economic criteria for sustainable regional development with the features of emissions and waste management technologies within the industry. Studying the production activities and analyzing the impact of KPO on the development of the region in the period from 2012 to 2022 made it possible to construct a matrix of the restorative potential of a circular economy in the region. A model for the formation of a circular economy was proposed, which is based on the introduction of innovations, investments in environmental protection, and the use of the best available technologies for reinjecting gas into the reservoir, increasing energy efficiency, and recycling waste, which resulted in a significant (2.2 times) reduction in the amount of air pollution in the region. According to the forecast model (2024–2028), it was determined that, in the case of maintaining certain independent indicators’ dynamics of development, the level of atmospheric emissions by KPO could be reduced by two times. The results of this work prove that further studies on the problems associated with reducing atmospheric pollution within the framework of the formation of a circular economy are quite promising. Additionally, the results of this study are interesting and may be useful for the implementation of measures to manage air quality in the region by managers, heads of organizations, state and local authorities, and researchers interested in promoting the Environmental Social Governance (ESG) concept of sustainable development.

Thermal and environmental analysis of Cucumis sativus drying in a mixed mode solar dryer with combined sensible and latent heat energy storage

This study presents a thermodynamic analysis of a mixed-mode solar dryer incorporating both sensible and latent heat energy storage materials. Black pebbles were utilized for sensible heat storage, while Lauric acid was selected for latent heat storage. The integration of these energy storage materials significantly enhanced the thermodynamic performance of the dryer, achieving a peak energy efficiency of 14.2% and a 53% increase in average energy efficiency. Additionally, the inclusion of latent heat storage in the collector resulted in the highest recorded collector energy efficiency of 84.6%. Exergy analysis indicated a maximum exergy efficiency of 51.3%, with an average exergy efficiency of 34.3% for the dryer. The implementation of combined thermal energy storage led to a 50% reduction in drying time. Sustainability assessments demonstrated that integrating both sensible and latent heat storage improved energy utilization while minimizing losses, thereby enhancing the overall sustainability and productivity of the solar dryer.The environmental analysis estimated a CO₂ mitigation potential of 83.97 tonnes per year, with a corresponding carbon credit value of $419.85. The system exhibited a remarkably low energy payback period of 1.82 years when operated with both thermal energy storage materials. This research underscores the potential benefits of combining latent and sensible heat storage in solar drying applications, highlighting its contribution to sustainability and the environmental advantages of solar thermal systems.

AI-Driven Computational Frameworks: Advancing Edge Intelligence and Smart Systems

The rapid advancements in Artificial Intelligence (AI) and Edge Computing are transforming modern computing paradigms by enabling real-time processing, low-latency decision-making, and enhanced intelligence in smart systems. This paper presents an AI-driven computational framework that integrates Edge Intelligence (EI) with adaptive deep learning models to optimize data processing and decision-making at the edge. The proposed framework employs federated learning, neuromorphic computing, and reinforcement learning-based optimization to improve efficiency, security, and scalability in distributed edge environments. Key components include lightweight AI models for energy-efficient edge inference, privacy-preserving techniques using homomorphic encryption and blockchain, and self-learning architectures for adaptive real-time analytics. The study evaluates the framework’s performance in diverse applications, including smart healthcare, autonomous vehicles, and industrial IoT, demonstrating significant improvements in computational efficiency, network resilience, and response time compared to traditional cloud-based architectures. Comprehensive simulations and real-world case studies validate the feasibility and effectiveness of the proposed approach, showing a 35% reduction in latency, a 30% increase in energy efficiency, and a 50% improvement in decision accuracy in edge-enabled smart systems. This research highlights the critical role of AI-driven computational frameworks in advancing next-generation intelligent computing, paving the way for autonomous, secure, and efficient edge-based smart environments.

Is there a nexus between tourism development and energy transition in Asia-Pacific countries?

ABSTRACT This paper examines the impact of tourism development on energy intensity and clean energy in 23 Asia-Pacific countries from 2000 to 2019. To ensure robust findings, both ordinary least squares (OLS) and quantile regression were applied. The findings reveal a long-term equilibrium relationship between tourism development, urbanisation, income, industrialisation, and energy transition. OLS results indicate that tourism contributes to energy efficiency while reducing clean energy use. Similarly, the quantile regression results are consistent with the OLS findings, particularly at the median and higher quantiles. These results suggest that while tourism enhances energy efficiency in Asia-Pacific countries, it limits the adoption of clean energy. To address this dual impact, policymakers should implement incentives that capitalise on tourism sector’s ability to increase energy efficiency (supporting SDG-7.3), while also removing barriers to clean energy adoption (fulfilling SDG-7.2). Additionally, governments can collaborate with tourism stakeholders to develop sector-specific policies and promote environmentally responsible tourist practices.

Development of directions for increasing the efficiency of innovation management taking into account decarbonization trends in the context of international cooperation

The most important area in the fight against negative climate changes and ensuring sustainable development is the decarbonization of the economy. The object of this study is the development trends in decarbonization to enable effective innovation management processes under the conditions of international cooperation. Decarbonization involves reducing carbon dioxide emissions by switching to renewable energy sources, increasing energy efficiency, and introducing innovative technologies. As a global trend, it requires coordinated actions at all levels – from national governments to the private sector and international organizations. The possibilities of ensuring decarbonization through the use of climate-neutral energy complexes have been assessed. A feature of the approach is the use of statistical analysis to identify trends in the development of decarbonization of the economy. This has made it possible to prove with a probability of 98.6 % that if the trends of recent years regarding the increase in the number of operating nuclear reactors prevail, by the end of 2030 their number in the world will reach 489 units. The practical value of using wind power plants of different capacities and small modular reactors (SMRs) to provide electric vehicles with energy has been substantiated. This is confirmed by calculations of the number of cars that can be supplied with electricity from wind stations of various capacities at certain values of the capacity factor. In contrast to conventional approaches, determining the areas of innovative management allows the implementation of decarbonization processes under the conditions of international cooperation. The use of wind power plants of various capacities is of practical interest; the expediency of using small modular reactors and supplying electric vehicles with energy has been assessed

A Comparative Analysis of Renewable Energy Policies in Local Governments

This study evaluates the importance and impacts of renewable energy sources in local government policies. The risk of depletion of fossil fuels, climate change and the need for environmental sustainability have accelerated the orientation of local governments towards renewable energy in the world. Local governments have various responsibilities such as increasing energy efficiency, reducing carbon footprint and raising public awareness on green and renewable energy sources by taking a leading role in energy transformation processes. In this article, local policies for renewable energy are analysed in terms of various legal regulations, financial incentives, community participation and technological developments. In addition, the integration of renewable energy at the local level is discussed with different examples from around the world and the contributions of these policies on sustainable development are discussed. The purpose of this study, explores the critical role that local governments play in promoting and implementing renewable energy initiatives. It highlights how local policies can significantly influence the adoption of renewable energy technologies, improve energy efficiency and contribute to sustainable development goals. The paper discusses various strategies used by local governments, such as incentives for renewable energy projects, zoning regulations that favour clean energy infrastructure, and community engagement efforts to raise awareness about the benefits of renewable energy. In addition, it addresses the challenges faced by local governments, including funding limitations, regulatory barriers, and the need for intergovernmental co-operation. In the study, qualitative method was used, document analysis and literature review were conducted. Overall, the paper emphasises that effective local government policies are necessary to promote the transition to renewable energy sources and achieve long-term environmental and economic benefits for communities. Furthermore, this study reveals that local governments have a strategic position in the energy transition.

THE ROLE OF HUMAN RESOURCES IN THE DEVELOPMENT OF THE GLOBAL CONSTRUCTION MARKET

The article explores how the development of the global construction market influences the increasing role of human resources, which are essential for the industry. The study examines the global construction market, noting that the construction sector currently plays one of the key roles in the global economy. The value of leading construction companies ranges from USD 13 billion to USD 48 billion. Overall, the global construction market accounts for 14.2% of global GDP. The research indicates that the growth rate of the market will continue to show a positive trend in the future, mainly due to catalysts such as risks leading to damage and loss of construction projects, population growth, urbanisation, sustainable development, government support, industrialisation, the development of smart cities, and infrastructure investments. The study reveals that the damages to construction projects caused by global emergencies are constantly increasing, necessitating reconstruction and renovation. Population growth stimulates the financing of residential and other real estate construction, such as hospitals, schools, roads, shopping centres, and other infrastructure. Urbanisation processes have led to an increased demand for new housing and social infrastructure. The need to reduce the harmful impact on the environment has led to the implementation of sustainable development programs, under which construction is financed with reduced carbon emissions and increased energy efficiency. Government support has significantly influenced the development of the global construction market, manifested in the financing of affordable housing. Innovations in design and construction have necessitated the financing of modern construction. The authors found that the factors studied have a significant impact on the development of the global construction market, which will lead to the need to enhance human resources. It is proposed to attract the younger generation to the industry, which will create a new construction culture that meets modern requirements.

Energy efficient hierarchical clustering based dynamic data fusion algorithm for wireless sensor networks in smart agriculture

A potential strategy to increase agricultural yields and maximize resource use has emerged: smart agriculture. In order to monitor numerous environmental characteristics, wireless sensor networks (WSNs) are essential. Individual sensor data may be noisy, redundant, and not correctly reflect the status of the farm as a whole. The energy constraints of WSN nodes and the need for accurate event detection, however, make it difficult to develop reliable and efficient systems. This research proposes a fresh approach to these issues by using hierarchical clustering-based dynamic data fusion techniques for WSNs in smart agriculture. In order to increase energy efficiency and event detection precision in smart agriculture, this study suggests employing dynamic data fusion for WSNs that is based on hierarchical clustering. The hierarchical clustering technique is used initially in the suggested method to group sensor nodes into clusters. A dynamic data fusion method is used to collect and fuse data inside each cluster, generating indicative information about the cluster’s status. This guarantees effective network resource utilization while minimizing data redundancy. In order to classify and anticipate events, the Extreme Learning Machine (ELM) technology is also used, allowing for the real-time identification of key events. The experimental outcomes show considerable increases in energy effectiveness and event detection precision, which makes this strategy an important contribution to the field of smart agriculture. The proposed model is implemented in Python software and has an accuracy of about 99.54% which is 1.81% higher than other existing methods like CH selection, K- prediction and data aggregation.