Energy Efficiency最新文献

At a time when India is making earnest efforts to achieve sustainable development goals, setting decarbonisation targets, this study is timely as it gives important insights for policy making. Electricity-energy saving behaviour is one of the sustainable goals to be achieved, as a part of larger sustainable development goals. This study explores the factors influencing electricity saving intentions and behaviour of generation Y and Z are supportive of sustainable development goals. We also tested the moderating effects of habit on the relationship between intention and behaviour. To achieve the objective of the study we used extended theory of planned behaviour (eTPB) as conceptual framework. The data of was collected from 347 respondents. Partial least square structural equation modeling (PLS SEM) and necessary condition analysis (NCA), reveals that, information publicity, perceived behavioural control, attitude, and personal norms are sufficient conditions for electricity saving intention. They have a significant positive impact on intention. Intention in turn proved to be a significant determinant of actual behaviour and proved to be a necessary condition for a high level of actual behaviour. Habit showed a significant moderating effect on the relationship between intention and behaviour. The combination of both PLS-SEM and NCA techniques lends novelty to the study, as it brings to the fore the difference between sufficient and necessary conditions required to achieve the desired outcome.

Historic buildings reflect the architectural features and construction techniques of the era to which they belong. They provide detailed, concrete information about natural ventilation, lighting, drainage, heating, and cooling, namely, about the functional systems of the region and the era, and thus serve as an important source of inspiration and as a guide and contribution to the sustainable design of buildings in today's fast-growing construction industry, where the protection of the environment and human health has become increasingly important. Therefore, this study aims to develop a score-based comprehensive method to evaluate the unique functional systems of historic buildings in order to investigate and determine their sustainable and energy-efficient characteristics and utilization in detail. In addition to literature and archival research, field research and national and international standards on sustainable design and energy efficiency of historical buildings were used to develop the method, which was tested on buildings of the First National Architectural Period in Konya. The results showed that the design features of the historic buildings such as settlement and orientation, building form, envelope, elements and heating, cooling, ventilation, and lighting systems were passively designed and offered sustainable solutions and opportunities in sustainable design and operation. Overall, it is shown that unique energy-efficient properties of functional system solutions in historic buildings can be revealed through a score-based methodology that can help to make more accurate decisions in the preparation and implementation of conservation and restoration projects, thus contributing to the sustainable preservation of buildings.

Following over a decade and a half of austerity measures, and with costs of delivering statutory duties soaring, UK local authorities’ resources and capabilities to deliver net zero are diminishing. Decarbonisation funding provided by central government, meanwhile, is awarded competitively. To secure long-term, place-based net zero investments under these unfavourable circumstances, UK local authorities are increasingly turning to public procurement. A prominent example is Bristol City Leap, a Joint Venture Company procured by Bristol City Council between 2018 and 2022 to deliver around £1bn of investment in energy infrastructure and service delivery over 20 years through a concession agreement. Drawing on workshops and interviews with key stakeholders and experts, this paper examines the risks and opportunities of procurement and early-stage delivery of this public–private-partnership model. Using insights from transaction cost economics, it finds that this agreement has significantly increased net zero investment in return for increased risk and transaction costs. To ensure successful, just, and equitable delivery of promised place-based net zero investments, significant procurement capabilities, careful due diligence procedures, continuing institutional oversight, and independent measurement and verification are required.

Electric motors service longer than is generally assumed, resulting in large numbers of inefficient motors remaining in service. Replacing them faster would free up additional energy savings – a contribution not to be overlooked in view of the energy efficiency objectives set out at COP28. The savings potential is even greater if the entire motor system is optimized at the same time. The EU-funded initiative EU-MORE conducted a review of current policy measures stimulating motor replacement in EU Member States, on the basis of publicly available data sources and with the help of national experts. The identified measures were analysed for their approach, impact, and the lessons learned. The review revealed that support measures of a financial nature formed the large majority, while it is questionable whether these are still the most effective in times of high electricity prices. The lack of insight into the benefits of motor replacement is much less addressed. A significant proportion of the identified measures address industrial energy efficiency in general, often without mentioning motor replacement as an eligible measure. Only a few countries have developed policy instruments specifically promoting a system approach to motor replacement. The review is followed by recommendations aiming to tackle these shortcomings and build on the success factors of existing policies.

Rapid urbanization and a growing population are intensifying the challenges of climate change, with the construction sector emerging as a significant contributor. As this sector expands at an accelerated pace, its rising energy demands urgent attention and sustainable intervention. In this context, the present study explores the sustainability potential of an integrated modeling approach by utilizing it in a residential building located in diversified climatic conditions of Jammu, Jammu Kashmir, India. The study presents a comprehensive framework by combining traditional architectural practices with advanced simulation tools to assess building performance across various design configurations and lifecycle stages. A Benefit-Cost (B/C) analysis is also introduced to quantify the trade-offs between building performance and spatial modifications at the preconstruction phase, offering a holistic metric for design efficiency. The findings reveal significant variations in performance across different window-to-wall ratios (WWR) and geometry configurations, with square windows at 20% WWR demonstrating the highest B/C ratio. This configuration shows substantial potential in developing energy-efficient and cost-effective structures. Additionally, the implementation of energy-efficient strategies reduced the annual energy consumption to 49570 kWh, representing an approximate savings of 30.1% compared to the reference case. The study provides actionable insights for data-driven decision-making that can be used for retrofitting as well as new construction.

Against the backdrop of China’s carbon peaking and neutrality objectives, this study aims to systematically identify the drivers of China’s carbon intensity (CI) and quantify their sector-specific impacts. Using a multiplicative Logarithmic Mean Divisia Index (LMDI) decomposition model, we analyze provincial panel data from 2000–2020 to evaluate the contributions of six factors—carbon coefficient, fossil energy structure, nuclear energy share, renewable energy share, energy intensity, and industrial structure—to CI changes, and further attribute these effects to 41 economic sectors via an attribution model. The results showed that: (a) the decline of energy intensity is the leading factor to promote the reduction of carbon intensity in China; (b) The transformation of fossil energy and industrial structure inhibited China's carbon intensity reduction; (c) Renewable energy consumption and carbon coefficient promoted carbon intensity reduction in some degree; (d) Energy intensive sectors have had an important impact on China's carbon intensity. Therefore, targeted interventions in energy-intensive sectors, coupled with the deployment of low-carbon energy-saving technologies, energy efficiency improvements, and renewable energy promotion, are effective strategies to promote the reduction of China's carbon intensity.

Power systems are undergoing extensive changes to pursue the goal of a carbon-free environment. In the course of these changes, the expansion of the penetration of renewable energy sources (RES) and the digitalization of demand-side electrical equipment causes a sharp increase in uncertainty and variability in power systems. As a result, it is difficult to plan and operate the system to balance supply and demand, which endangers its sustainability. To deal with these challenges in different studies, demand-side flexibility (DSF) in power systems has been suggested. Many factors, including market prices, behavioral and cultural habits of the community, etc., affect DSF. Therefore, in the literature, considering these factors, various approaches and methods have been developed to assess demand-side flexibility. In a general category, they can be divided into top-down and bottom-up approaches. In the top-down approach, the demand side is considered a large flexible resource and does not pay attention to the flexibility of the DSF resources. In the bottom-up approach, the flexibility of the DSF resources is generalized to the national or regional level. In these approaches, very big assumptions are made, which challenges the assessment results. In this article, a comprehensive overview of DSF potential and DSF assessment approaches will be done, and the requirements and methods development approaches will be proposed.

This paper introduces a novel decentralized peer-to-peer (P2P) energy trading model leveraging a Proximal Policy Optimization (PPO) driven deep reinforcement learning (DRL) approach, to optimize energy transactions among smart homes within a smart grid environment. The proposed model aims to minimize energy costs while promoting efficient energy consumption patterns through dynamic pricing schemes. A new policy function has been designed to enhance the training and real-time working efficiency of the PPO-based P2P energy trading framework, enabling faster convergence and improved trading strategies. By learning optimal energy trading policies through continuous interaction with the environment, the model integrates historical consumption data and real-time market dynamics to deliver substantial cost savings. Experimental evaluations reveal an average reduction of 45% in energy expenses for participating households compared to conventional methods. Additionally, the proposed framework demonstrates robustness and adaptability across diverse market conditions and consumer preferences, ensuring scalability and practical applicability in real-world scenarios. This study underscores the transformative potential of reinforcement learning in advancing decentralized energy trading systems, offering a sustainable and cost-effective solution for modern energy markets.

Recent studies have used algorithm tailoring on digital platforms to provide household energy-saving advice. Such ‘recommender systems’ have successfully used the psychometric Rasch model as an advice algorithm, matching energy-saving measures in terms of their difficulty to consumers’ ability levels. While these previous studies indicated positive user experiences, tailored advice did not lead to higher savings overall; not even when also using persuasive nudges, such as displaying social norm percentages in the system. One possible reason for these results was that the system was used exploratively, allowing users to pick energy measures as they liked without tapping into goal setting or value-based motivational frames (e.g., signposts). In this study, 202 participants used and evaluated our ‘Saving Aid’ Rasch recommender system, choosing energy-saving measures they would like to perform at home. Through a 3 × 2-between subject design, we examined whether guided goal setting and signposts (kWh/Euro/CO2) affected user experience and energy savings. Following the signpost literature, we examined the moderation of these effects by user values, such as environmental concern (New Environmental Paradigm (NEP) score). A structural equation model analysis revealed that goal setting did not affect outcome variables, while signpost framing had varying effects, although these were not in line with prior expectations. Still, the overall system remains promising, with users achieving a 316 kWh yearly savings with the chosen recommendations.

Demand Side Management (DSM) has emerged as a key strategy in smart grids due to its flexibility and cost-saving potential, helping consumers manage and reduce their electricity expenses. Within the energy market, stakeholders such as consumers, demand response aggregators, and utility providers aim to enhance their respective profits. However, aligning these interests simultaneously poses significant challenges. To address this, the present work integrates the concepts of DSM and Dynamic Economic Dispatch (DED) into a unified tri-objective optimization framework that accounts for the variability inherent in solar and wind power generation. The proposed DSM-DED model is tackled using the Class Topper Optimization (CTO) algorithm. The objective is to efficiently schedule both demand and generation over a 24-hour horizon to minimize peak loads, improve the load factor, cut operational costs, reduce consumer bills, and ensure equitable profit distribution among all market participants. Prior to integration with the smart grid model, wind speed and solar irradiance are forecasted using the Weibull and Lognormal probability distribution functions, respectively. Simulation results underscore the importance of effective DSM strategies and renewable energy integration in enhancing the overall economic and operational performance of smart grids.