Energy Efficiency最新文献

Energy consumption has been identified as the key contributor to carbon dioxide emissions and the depletion of natural resources. Promoting the purchase of energy-efficient appliances is crucial for environmental sustainability. While numerous studies have explored consumer purchase decisions regarding energy-efficient appliances, the literature remains broad and fragmented. Anchoring in the Theory–Context–Characteristics–Methods (TCCM) framework, this systematic literature review aims to synthesize the available research and offer a complete overview of the current state of research into consumer purchases of energy-efficient appliances. Conducting a thorough analysis of 142 empirical studies, this study uncovers that the Theory of Planned Behavior is the most frequently used framework, and most studies adopted quantitative methods and focused on emerging Asian countries (e.g., China, Pakistan, and Malaysia). In addition, this study also points out socio-demographic, psychographic, situational, and marketing-related factors that affect consumer purchase behavior toward energy-efficient appliances. Based on these findings, this study proposes a research agenda for scholars to advance the research field and practical implications for policymakers, manufacturers, and marketers to encourage the adoption and purchase of energy-efficient products.

Hybrid vehicles present promising application prospects, combining the tradition and autonomy of combustion engines with the reliability, efficiency, and environmental appeal of electrification. In Brazil, the eighth-largest vehicle producer globally, up to 70% of the fleet comprises flex-fuel vehicles (FFVs), capable of operating with gasoline E27 (27% anhydrous ethanol), hydrous ethanol E100, or any blend, representing a significant portion of the country’s transportation matrix. Energy recovery is an important strategy for power conservation and increasing the efficiency of hybrid vehicles that combine internal combustion engines (ICE) and electric engines with small-capacity batteries, commonly applied in low-level electrification. This paper proposes an overview of technologies for ICE energy recovery to improve hybrid FFVs efficiency, that should be the main strategy for comply new emission legislation in Brazil and others markets in the next years. It employs a two-step approach: a literature review followed by patent analysis. The discussion analyzes advance technologies for recovering ICE energy like turbocharger, hybridization, exhaust gas recirculation (EGR) and Homogeneous Charge Compression Ignition (HCCI), applied to FFV ICEs. The United States, China and Germany are the countries with the highest number of patents registered in all ICE sub-technologies analyzed. Turbocharging appears to be a well-developed technology while HCCI still has many challenges to be overcome before it can be offered commercially. Hybrid FFVs vehicles using biofuel only or blended with gasoline offer a swift response to the energy transition in the mobility sector towards 100% electrification powertrain and provide a quicky solution for countries like Brazil.

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People spend most of their time in enclosed spaces, doing activities, which is why habitable spaces that meet minimum environmental comfort for occupants are sought. In Chile, high levels of energy consumption are needed to reach acceptable comfort levels and, therefore, high costs. For this reason, people use other strategies to maintain comfort, such as actions to adapt, changing elements in the space, or making personal changes such as wearing a sweater before turning on the heating. This study looks to analyze the relationship between the adaptive actions of occupants of dwellings in the Biobío Region and the architectural and constructive characteristics of their homes. Data collection was made using a survey where 72 households took part. The data analysis explored correlations between the studied variables. The results indicated that people perform personal actions before using any environmental ones, with the main factor behind this being the insulation level of the dwellings. This suggests that, in these contexts, people innately employ adaptive actions, generating energy savings.

With growing environmental concerns, the management of energy consumption has become a key focus for production firms. Consequently, increased attention has been directed by decision-makers and academic researchers toward modeling and forecasting energy consumption. However, only a limited number of studies have specifically examined electricity consumption modeling for industrial cases. To address this gap, an analysis was conducted to identify the factors influencing electricity consumption in a specific case from the confectionery industry in France. Multiple linear regression and multiple exponential regression techniques were utilized to establish an equation correlating consumption with various influencing factors. By examining a comprehensive set of factors, including production volume, operating hours, ambient temperature, and water flow of air handling units, a thorough understanding of the relationship between these factors and electricity consumption in significant energy uses was achieved. The analysis of data collected from a representative confectionery plant revealed significant correlations between the influencing factors and electricity consumption. Based on the derived equation, a model was proposed to estimate electricity consumption using the values of these influencing factors. Additionally, a user-friendly interface was designed, enabling plant operators to apply the model with ease. The findings of this study motivated the company to explore decarbonization initiatives, leading to notable energy savings and a positive financial impact. These insights contribute to a deeper understanding of the drivers of electricity consumption in the confectionery industry and offer valuable guidance for developing strategies to optimize energy usage and enhance sustainability in this sector.

Smart energy applications can be powerful tools for two-way communication between energy providers and end-users. These apps offer real-time usage insights and present consumption data through intuitive and visually appealing interfaces. In parallel, these platforms allow users to provide ratings, reviews, and comments directly within or via app provision platforms, thereby helping energy providers improve their overall services. Long-term engagement with such energy apps could benefit sustainable energy consumption practices, as such digital platforms can empower users to make informed energy-efficient choices. Nonetheless, there is a lack of a systematic approach to analysing users’ engagement towards such smart energy apps and users’ opinions about their practical functionalities and features. This study examines how users interact with smart energy apps, assess their functionalities, and share their opinions through ratings, reviews, and comments. It offers unique insights into end-user perspectives through advanced text-mining techniques incorporating word analysis, sentiment analysis, topic modelling, and exploratory qualitative analysis. By analysing over 3,000 publicly available user-generated reviews, the research identifies patterns such as monitoring energy consumption, managing appliances, and integrating smart home features like EV charging. The findings highlight diverse motivations for app engagement, extending beyond cost savings to include empowerment, convenience, and alignment with sustainability goals. While users express predominantly positive sentiments about the app’s intuitive design, real-time feedback, and gamification features, reviews also reveal areas for improvement, emphasizing the importance of user-centred design in enhancing app usability and functionality. The findings provide actionable insights for improving app design, advancing smart energy solutions, and informing energy efficiency and conservation initiatives.

Managing energy costs in school buildings across Italy poses a significant challenge. Over time, various directives have aimed to reduce energy consumption and improve indoor air quality to enhance student and teacher performance. This study offers an innovative analysis of the thermal behavior of a school in a typical Mediterranean climate. The building envelope is designed to meet legal standards for thermal transmittance specific to its Italian climate zone. Using Termolog Epix 15 software, the study conducts dynamic annual and hourly simulations to assess operative temperature and relative humidity in classrooms, with the heating system turned off and varying air exchange rates. The results suggest that optimizing the building envelope can be more effective than installing traditional HVAC systems (heating, ventilation, and air conditioning) in warm climates, demonstrating the effectiveness of a mechanical ventilation system without heating or cooling. By analyzing the PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied) indices, the study demonstrates that it is possible to maintain acceptable thermo-hygrometric comfort through the only ventilation. During winter, with air changes per hour ranging from 1 to 5, classrooms can maintain temperatures between 21 °C and 23 °C, ensuring thermal comfort without heating. Typically, a heating system would operate for approximately 1071 h annually, excluding holidays. However, an effective ventilation system could eliminate the need for heating entirely. While maintaining optimal temperatures in the intermediate months (spring/autumn) and summer is more challenging, this concern is mitigated by the fact that schools are closed during summer holidays.

This paper investigates the dynamics surrounding energy efficiency measures in building renovation projects to understand how energy issues are dealt with at planning and design meetings for building renovations. Through a comparative analysis of case studies of residential building renovation projects in Sweden and Denmark, document studies, observations, and interviews were conducted. The study revealed barriers hindering the effective implementation of energy efficiency practices and discussed how these can be overcome. Despite a growing recognition of the importance of energy issues, professionals often prioritise design aspects and technical installations over energy saving or efficiency. Conventional renovation practices limit the adoption of more innovative or advanced energy-efficient solutions. Financial constraints and a lack of focus on energy performance were significant barriers to the implementation of more progressive measures. Energy calculations as well as energy consultants are not given space and time at the project meetings. Clear guidance and alternative financing mechanisms are crucial for overcoming these hurdles and promoting sustainable building practices.

The Non-Intrusive Load Monitoring (NILM) technique has emerged as an efficient technique for conserving power and enhancing energy efficiency in residential buildings. This paper introduces a NILM disaggregation framework based on the multi-target regression approach, which is particularly suitable for real-time energy disaggregation. For this purpose, this work proposes a new Parallel Hybrid CNN-GRU (PHCNN-GRU) deep learning model for NILM disaggregation tasks. This technique takes advantage of the ability of Convolutional Neural Networks (CNN) to efficiently process spatial data and the excellent capability of Gated Recurrent Units (GRU) to process complex time-series data, due to their ability to retain memory of prior inputs. The proposed model has been tested and evaluated using two low-frequency benchmark databases: the UK-DALE database and the AMPds database. The experimental results demonstrate the effectiveness of the proposed model for energy disaggregation. Specifically, when using the UK-DALE database, the proposed model achieves an overall F1-score of 86.86% and an estimation accuracy of 87.16%. Moreover, when utilizing the AMPds database, the proposed model achieves an overall F1-score of 94.21% and an estimation accuracy of 94.13%. Furthermore, to better assess the performance of the proposed model, a noise signal was added to the input data. The obtained results indicate the effectiveness and robustness of the proposed model, even in the presence of noise.

This paper presents the configuration and performance of a Low Enthalpy Geothermal System successfully installed and utilized in the Mediterranean climate zone. Additionally, it examines the performance of different types of Ground Heat Exchangers (GHE), all installed in the same System. The Ground Source Heat Pump (GSHP) of the system consists of vertical ground heat exchangers (GHEs) in five different configurations, one double helicoidal coil in a well and an open loop (well) system. The entire system is constantly monitored by a Building Management System (BMS) that records the energy, volume flow, incoming and outgoing temperature at critical points of the system. Based on the recorded values, the performance of the System was analyzed in a heating and a cooling working mode, after examining the power flows in and out from critical points of the System. Results show higher heat exchange values inside the open well, both in heating and cooling mode suggesting the usage of this type of GHE, where applicable. Additionally, the electric power consumed by the chillers which are the largest electricity consumers within the System, is approximately five times lower than the power placed in the building by the Geothermal System (SCOP between 4.5 and 5). In terms of primary energy savings, we can say with confidence the GSHP systems working under Mediterranean climate zone conditions, can be consider as high efficiency solutions, verifying the theoretical efficiency given by the manufacturer of the GSHP.

The full penetration of electric vehicle (EV) is the support for China's dual carbon goal of decarbonizing urban transportation. However, the inadequate layout of EV service facilities, especially the battery swapping station network (BSSN), has hindered the development of EV in cities and the promotion of decarbonization of urban transportation. Therefore, in this paper, a novel BSSN two-stage layout model for EV is proposed by combining the characteristics of urban road net. At the first stage, a comprehensive utility model is constructed to select the candidate site nodes of BSSN by combining the node characteristics of urban road net based on Social Network Analysis (SNA). At the second stage, a layout model of BSSN is proposed to quantify the battery swapping demand and provide the optimal solution of BSSN, based on the comprehensive utility and the cost objective of the battery swapping station (BSS). Finally, the BSSN optimal solution is provided based on the layout model to cover the swapping demand within the Fourth Ring Road of Beijing as a case. In the case study, the consideration of multi-objective is proved to be effective. The optimal service radius for BSS is 5-7 km and 399,127.2t carbon emission reductions that can be generated based on this plan. In addition, the sensitivity analysis of BSS service radius, minimum station number and EV scale is carried out. The layout model of BSSN introduces comprehensive utility into the road net and optimizes the location of BSSN from the perspective of urban planning, and it is of significance to the planning of urban transportation.