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In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply–demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply–demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%. [ABSTRACT FROM AUTHOR]

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG. [ABSTRACT FROM AUTHOR]

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage. [ABSTRACT FROM AUTHOR]

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency. [ABSTRACT FROM AUTHOR]

Germany needs to reduce CO2 emissions from space heating in its old buildings to net zero by 2045 to fulfil its climate goals. However, direct CO2 reduction measures in existing buildings receive relatively little subsidy support from the federal government's German Development Bank, compared to generous subsidies for energy efficiency measures. This interdisciplinary paper evaluates this phenomenon by comparing costs and CO2 abatement effects of ever higher energy efficiency measures, alongside the costs of direct CO2 reduction through heat pumps and onsite photovoltaics. It uses a set of carefully selected reports on the costs and benefits of renovation to a range of energy efficiency standards in three common types of multi-apartment buildings in Germany, updating these for 2024 construction, energy, and finance costs. The cost of the CO2 saved is extremely high with energy efficiency measures and absurdly high with the highest energy efficiency standards, up to 20 times the cost of CO2 abatement through other means, such as offsite renewables. This reduces markedly with onsite CO2 reduction measures. This paper sets this analysis in the context of asking what social, cultural, and discursive factors extol energy efficiency so highly that policy tends to thwart its own stated goal of deeply reducing CO2 emissions. [ABSTRACT FROM AUTHOR]

This research paper examines how multi-angled façade systems improve and optimise energy performance compared to a flat façade and meet sustainability targets for lower energy use to align with UN SDGs 3, 11, 12, and 13. The multi-angled façade system does not tilt up and down. Instead, it employs two different window orientations on a vertical axis (left and right). The large portion orients more to the north to allow more daylight to penetrate inside the room, and the small part is oriented more to the south to provide passive solar heating. The investigations in this research paper were carried out using version 4.8 of the IDA ICE software, and the researchers evaluated the energy consumption, the energy action through the façade, and the building's inside operative temperature. The results of this paper present the simulation findings for primary energy consumption in different scenarios. For example, the researchers explain that one can save 6.3 kWh/(m2·year) when using a multi-angled façade system compared to a flat façade. This is in addition to improving the thermal indoor climate that results from using the façades. The conclusions of the research show that the façade with multiple angles maximises using daylight and optimises solar power, thus avoiding overheating issues. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for modeling hydropower plants (HPPs) with and without pumping capability in resource adequacy assessment studies. The proposed method is based on the premise that HPPs should maximize their contribution to system adequacy within their technical constraints by using the energy reserves in their upper reservoirs without significantly deviating from their market schedule. The approach of this paper differs from the conventional operating policies for incorporating HPPs into resource adequacy assessment studies, which either adhere to a fixed market schedule or perform peak shaving, and are inelastic to real-time events or do not resort to realistic temporal correlations between natural water inflows on upper reservoirs and the water discharge needs to cover demand peaks, respectively. The modeling approach focuses on large-reservoir HPPs with natural inflows and is generic enough to deal with both stations incorporating pumping capabilities and those without. It utilizes the state-of-the-art Monte Carlo simulation technique to form the availability of system assets and determine the loss of load incidents. The market schedule and level of reservoir fulfillment for the HPPs are retrieved from a cost-optimal power system simulation algorithm executed offline before the application of the resource adequacy assessment. The effectiveness of the proposed methodology is demonstrated through its implementation in a case study of a power system experiencing different levels of adequacy, comparing the obtained results with various traditional HPP modeling methods from the literature. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energy sources introduces significant fluctuations in power generation, demanding enhanced regulatory capabilities to maintain the balance between power supply and demand. To promote multi-energy coupling and the local consumption of renewable energy, integrated energy systems have become a focal point of multidisciplinary research. This study models adjustable sources, networks, and loads within electric–thermal integrated energy systems as energy storage entities, forming virtual energy storage systems to participate in the optimization and scheduling of integrated energy systems. This paper investigates the modeling and control strategies of virtual energy storage systems within electric–thermal integrated energy systems. Initially, it introduces the definition, logical architecture, and technical connotations of virtual energy storage. Next, it models temperature-controlled loads as virtual energy storage systems and compares them with traditional energy storage systems, analyzing their characteristic differences and summarizing virtual energy storage system modeling methods and characteristic indicators. This paper then focuses on the specific applications of virtual energy storage systems in four typical scenarios. Finally, it explores the future development directions of virtual energy storage. [ABSTRACT FROM AUTHOR]

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial. [ABSTRACT FROM AUTHOR]

Chiller fault diagnosis plays a crucial role in optimizing energy efficiency within heating, ventilation, and air conditioning (HVAC) systems. The non-stationary nature of chiller fault data presents a significant challenge, as conventional methodologies often fail to adequately capture the relationships between non-stationary variables. To address this limitation and enhance diagnostic accuracy, this paper proposes an improved graph deviation network for chiller fault diagnosis by integrating the sparse cointegration analysis and the convolutional block attention mechanism. First, in order to obtain sparse fault features in non-stationary operation, this paper adopts the sparse cointegration analysis method (SCA). Further, to augment the diagnosis accuracy, this paper proposes the improved graph deviation network (IGDN) to classify fault datasets, which is a combination of the output of a graph deviation network (GDN) with a convolutional block attention mechanism (CBAM). This novel architecture enables sequential evaluation of attention maps along independent temporal and spatial dimensions, followed by element-wise multiplication with input features for adaptive feature optimization. Finally, detailed experiments and comparisons are performed. Comparative analyses reveal that SCA outperforms alternative feature extraction algorithms in addressing the non-stationary characteristics of chiller systems. Furthermore, the IGDN exhibits superior fault diagnosis accuracy across various fault severity levels. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of challenges and available solutions for exterior insulated panels suitable for deep energy retrofits of existing building envelopes. The analysis covers a review of available technologies that provide flexible retrofit insulated panels suitable for multiple climates and building typologies. Moreover, the paper proposes a new design for insulated retrofit panels that account for the majority of identified technical risks including cost, architectural diversity, climate variations, structural concerns, moisture resilience, air sealing, and water sealing. Additionally, the proposed design can be easily installed with minimal disruption to the occupants. A series of parametric and optimization analyses is carried out to identify the optimal design specifications for insulated panels suitable for deep retrofits of existing US housing stocks. The analysis results show that the optimal design criteria for the insulated panels can reduce heating and cooling energy consumption by up to 80% and HVAC capacities by 70%. Moreover, the results indicate that these insulated panels are highly cost effective for retrofitting US housing units located in cold climates. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

With the increasing global focus on energy efficiency and environmental sustainability, intelligent building materials such as thermochromic glazing have emerged as a hot topic of research. The intent of this paper is to explore the utilization of gel-type thermochromic glazing within the realm of architectural energy conservation calculations. It conducts an exhaustive examination of the material's attributes, its capacity for energy savings, and the obstacles encountered in real-world applications. Through simulation studies and case analyses, this paper assesses the energy efficiency of gel-type thermochromic glazing across various climates and suggests strategies for optimization. The study revealed that the incorporation of gel-based thermochromic glazing leads to a marked reduction in energy usage within buildings, an improvement in indoor comfort levels, and significant environmental advantages. [ABSTRACT FROM AUTHOR]

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability. [ABSTRACT FROM AUTHOR]

The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions. [ABSTRACT FROM AUTHOR]

Renewable energy has emerged as a promising solution to address the challenges of climate change, energy security, and socio-economic development. South Africa, with its abundant renewable energy resources, has made significant strides in the utilization of renewable energy over the past decade. This paper provides a comprehensive review of the progress of renewable energy advancement in South Africa, examining the policies, initiatives, and achievements in various renewable energy sectors. This study explores the country's transition from a heavily coal-dependent energy system to a diversified and sustainable energy mix. It analyses the growth of renewable energy technologies, such as wind power and solar photovoltaic (PV), highlighting the key milestones, challenges, and opportunities. Furthermore, this paper discusses the role of government support, regulatory frameworks, and private sector investments in driving renewable energy deployment in South Africa. Finally, it identifies the prospects and potential areas for further advancement in the renewable energy sector. This review aims to contribute to the understanding of South Africa's renewable energy journey and provides valuable insights for policy-makers, researchers, and stakeholders involved in the sustainable energy transition. [ABSTRACT FROM AUTHOR]

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region's abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season. [ABSTRACT FROM AUTHOR]

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid's ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. [ABSTRACT FROM AUTHOR]

This document is a compilation of research papers that explore the topics of Smart Cities and Positive Energy Districts (PEDs). The papers cover a range of subjects including energy efficiency, renewable energy communities, smart urban management, and financial schemes for sustainable neighborhoods. The findings emphasize the importance of energy renovation, potential energy and financial savings, and the need for collaborative approaches and policy direction to achieve sustainable urban development. The papers also provide insights into innovative solutions and technological advancements that can contribute to the realization of carbon-neutral and sustainable cities. This document serves as a valuable resource for library patrons conducting research on energy efficiency and sustainable building practices. [Extracted from the article]

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Transitioning to clean cooking fuels is not only part of achieving SDG7 but also makes a significant contribution to mitigating climate change by reducing carbon emissions. Research projects and pilots across a number of countries in Africa and South Asia have been exploring the suitability and energy performance of different cooking appliances and fuels. The paper presents the first statistical analysis across these multiple datasets to determine the range of energy required to cook dishes using different technologies and fuels. The paper draws out distinctions between African and Asian dishes, notably the impact of energy-intensive dishes prepared mostly in Africa. The paper demonstrates that the standard efficiency-based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices, so a novel alternative approach based on specific energy consumption and termed energy ratios is developed. Charcoal stoves are shown to use 15 times as much energy as electric pressure cookers (EPCs) to cook African dishes, and a detailed review of how the EPC works explains why this should be. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels and also for estimating household cooking costs. Fuel and electricity prices from studies show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy-efficient electric cooking devices to defy the conventional wisdom of the energy ladder. [ABSTRACT FROM AUTHOR]

This paper explores the significant role of Wastewater Treatment Plants (WWTPs) in achieving environmental sustainability, with a particular focus on enhancing energy efficiency, resource recovery, and water reuse. WWTPs are crucial for removing pollutants and recovering resources from wastewater, thereby protecting public health and biodiversity. However, they are also associated with high operational costs, substantial carbon footprints, and energy-intensive processes. This article delves into various strategies and technologies to overcome these challenges, aiming to transform WWTPs from energy consumers to energy-efficient resource recovery hubs. Techniques such as anaerobic digestion and the use of advanced oxidation processes and microbial fuel cells are investigated for their potential in energy recovery and efficiency enhancement. Success stories from around the globe are highlighted to demonstrate the feasibility of transitioning to energy-positive WWTP operations. The integration of water reuse systems is also discussed, highlighting recent advancements that enable treated wastewater to be repurposed for agricultural, industrial, and potable uses, thereby promoting sustainability and water conservation. This paper emphasizes the importance of integrating cutting-edge energy management practices to minimize environmental impacts, reduce operational costs, and contribute to a more sustainable water sector. [ABSTRACT FROM AUTHOR]

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less. [ABSTRACT FROM AUTHOR]

In the context of sustainability, the integration of renewable energy into industrial processes not only minimizes dependence on fossil fuels but also contributes to the efficient management of industrial waste. By transforming organic waste, including agri-food and urban waste, into biogas, green energy can be generated, thus reducing the impact on the environment and closing the loop of material used in the economic circuit. Thus, a sustainable system can be promoted, where resources are continuously reused and exploited. Statistical methods and a decision tree with the Classification and Regression Trees (CRT) algorithm were employed to analyze data. The paper focuses on the importance of industrial waste and biogas for the generation, transformation, and consumption of energy in the EU (European Union)-27 countries. To provide a thorough analysis, we have divided these countries based on real gross domestic product (GDP) per capita, grouping them above/below the annual average for the period 2012–2021/2022. Descriptive statistics revealed observable differences between the two groups, but the paper aimed to provide evidence regarding the existence of these differences as statistically significant. Using the Kolmogorov–Smirnov test, the non-normal distribution of the data was confirmed, requiring non-parametric inferential methods. The Mann–Whitney U test revealed statistically significant differences between the two groups for all the studied variables. This comprehensive approach highlights the distinct energy-related characteristics influenced by economic development in the EU-27. [ABSTRACT FROM AUTHOR]

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

Cultural institutions hold a unique position of influence, serving as hubs of education and knowledge dissemination for the people they serve. Embracing sustainable energy use in buildings in zoos and botanical gardens is a commitment to environmental responsibility that reinforces these institutions as trusted sources of information and community leaders on climate change, one of the most vital issues of our time. Sustainable energy solutions can synergize operations with educational missions, allowing zoos and botanical gardens to lead by example and inspire visitors to adopt eco-friendly practices in their own lives. In this opinion paper, Phipps Conservatory and Botanical Gardens President and CEO Richard Piacentini discusses key elements in developing a sustainable building energy plan, reviews potential barriers to implementation, and makes a case for adopting regenerative thinking and new metrics for measuring success—citing example cases from Phipps' Center for Sustainable Landscapes (CSL) project. Additionally, the paper demonstrates how green building certification systems and peer-based resource networks like The Climate Toolkit can help guide institutions in the process. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm's robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm's stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications. [ABSTRACT FROM AUTHOR]

Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a fixed bed for generating alternative liquid fuel. This paper also compares alternative fuel characteristics, including higher heating value, viscosity, density, pour point, and flash point, with conventional petroleum fuels at optimal process conditions. The work adopted pyrolysis to produce liquid fuel at a temperature range of 400–550 °C and utilized LPG to provide a heat source. The liquid product (fuel oil) was collected, and non-condensable gas was flared. The liquid product was tested for various properties, and the results of the analyses show that alternative fuel has a higher heating value of 12.227 MJ/kg, density of 800 kg/m3, viscosity of 4.3 cP at 30 °C, pour point below −6.2 °C, and flash point around 137 °C, with a variation due to the volatile matters. To obtain favorable conditions for pyrolysis, some parameters, including bed temperature, sample quantity, average particle size, and operating time, were varied and analyzed. The physio-chemical properties made the alternative fuels isolated from conventional petroleum fuels due to the variation in distillation temperature. This work shows that the fuel oil generated from the pyrolysis of onion shells could be considered an alternative source of fuel. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

Studies demonstrate that the reduced frequency k is influenced by the incoming wind speed U0 and the rotor speed n. As a dimensionless parameter, k characterizes the stability of the flow field, which is a critical factor affecting the performance of vertical-axis wind turbines (VAWTs). This paper investigates the impact of k on the performance of straight-blade vertical-axis wind turbines (H-VAWT). The findings indicate that 0.05 is the critical value of k. The same k results in a similar flow field structure, yet the performance changes vary with different U0. A decrease in n or an increase in U0 leads to an increase in the average value and fluctuation of k, which subsequently reduces the rotor rotation torque Cm and decreases the maximum wind energy utilization rate Cpmax. This reduction in Cpmax weakens the stability of the flow field. Additionally, the high-speed area of the blade's trailing edge velocity trajectory at  θ = 0 ° ,  θ = 120 ° , and  θ = 240 °  expands with increasing range. Velocity dissipation in the high-speed area of the trailing edge affects the stability of the flow field within the rotor. [ABSTRACT FROM AUTHOR]

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered 'unregulated energy' in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap. [ABSTRACT FROM AUTHOR]

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants' behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm's assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies. [ABSTRACT FROM AUTHOR]

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO2 concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO2 concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom. [ABSTRACT FROM AUTHOR]