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Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the 'start time' of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hierarchical aerogels were fabricated by electrospun beads-on-string nanofibers. • The aerogels combine thermal insulation and spectral selectivity in one design. • The aerogels show great compression recovery and durable cooling performance. • Remarkable year-round energy savings can be achieved by using aerogel coolers. With the advancement of worldwide carbon neutralization, passive radiative cooling (PRC) has attracted tremendous interest in energy conservation by dissipating heat into the ultracold outer space without any electricity consumption. Despite some progress has been made in tailoring spectral properties for PRC, it still remains a crucial challenge in fabricating efficient and low-cost coolers for building energy saving. Herein, hierarchical cellulose-based aerogel coolers consisting of beads-on-string structural electrospun nanofibers are manufactured for all-day and all-region energy saving by combining radiative cooling and thermal insulation in one design. The hierarchically porous architectures and well-designed chemical compositions endow the aerogels with strong solar reflectance (∼0.974), high mid-infrared emittance (∼0.985), and ultra-low thermal conductivity (0.0285 W (m K)−1), achieving a sub-ambient cooling of ∼8.24 °C during the daytime and ∼7.41 °C during the nighttime. The aerogels also exhibit exceptional compressive resiliency, anti-aging, and self-cleaning properties, promising for durable cooling under harsh conditions. The building energy simulations show that about 23.1 kWh m−2 of the total energy consumption per year can be saved if the aerogel coolers are widely deployed as building envelopes in China. This work provides new perspectives for the development of advanced aerogel coolers for future energy saving applications. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is a tool used to evaluate the environmental impacts and resources used to manufacture a product. The present study proposes an innovative and unprecedented based on LCA of biodiesel production from the methyl transesterification of soybean oil catalyzed by eggshell-derived CaO and using the nonionic surfactant nonylphenol ethoxylate (NP6EO). Biodiesel was produced under mild conditions with a 1:4 methanol-to-oil ratio, 2 wt% CaO, 1 wt% NP6EO, and reaction times of 2 hours with surfactant and 3 hours without, with a yield of 95.18% and 97.75%, respectively. The life cycle impact assessment (LCIA) was performed using the SimaPro software and the Ecoinvent 3.6 database by implementing the CML-IA baseline method. Results indicated that the catalyst preparation process had the lowest environmental impact, while soybean oil used contributed the most across all impact categories. The surfactant-based process was 77% more eco-efficient than the non-surfactant process. Results also showed that surfactant concentration has more influence on impacts than additional electricity consumption due to a longer reaction time. Compared to traditional KOH-catalyzed biodiesel production, the use of CaO from eggshells and NP6EO demonstrated a lower environmental impact, suggesting this method is a promising alternative to conventional processes. [ABSTRACT FROM AUTHOR]

The high energy requirements of water electrolysis, driven largely by the sluggish kinetics of the oxygen evolution reaction (OER), hinder its widespread application for hydrogen production. This study presents an innovative alternative by replacing OER with the thermodynamically favorable furfural oxidation reaction (FOR), utilizing a CuPt nanowire (NW)/Cu electrocatalyst. The incorporation of Pt into Cu NWs significantly enhances FOR activity, enabling the production of value-added furoic acid alongside hydrogen gas at reduced voltages. The CuPt NWs/Cu catalyst also exhibits excellent hydrogen evolution reaction (HER) performance in alkaline media. By employing CuPt NWs/Cu as both the anodic and cathodic catalyst, we develop a two-electrode electrolyzer that integrates FOR and HER, allowing simultaneous hydrogen production at both electrodes. Operating at a current density of 10 mA cm−2, the system requires an exceptionally low cell voltage of 0.39 V and an electricity consumption of 0.47 kWh per m3 H 2 , demonstrating its efficiency for energy-saving bipolar hydrogen generation. [Display omitted] • CuPt nanowires with tunable Pt ratios are engineered. • Simultaneous production of furoic acid and hydrogen via furfural electrooxidation. • Bipolar hydrogen generation achieved in a furfural-water co-electrolysis system. • Ultra-low electricity consumption of 0.47 kWh per m3 H 2 for hydrogen production. [ABSTRACT FROM AUTHOR]

The use of reversible solid oxide cells (rSOC) as bi-directional Power-to-Gas (P2G) and Gas-to-Power (G2P) devices in microgrids with renewable energy sources has attracted considerable attention in the last years. The present study analyzes the energy management of a rSOC connected in a microgrid, considering hydrogen prices from the European HYDRIX market and electricity prices and demands from the Spanish electrical grid. The energy management strategy, based on model predictive control, determines the optimal path for transitions between SOE and SOFC according to the market. The strategy relies on a model including experimental rSOC transition times, thermal effects, and safety constraints to avoid undesired mode switching. The study was conducted in a scaled grid-connected system including 10 kW solar and wind renewable generation and a rSOC of 4.2–6 kW (SOFC-SOE). The aim is to assess the impact of different renewable energy sources on the performance of rSOC and on the resulting economic balance. The results show that an energy management strategy considering hydrogen markets can reach higher revenue, with increases of ≈ 4.6% for solar and ≈ 14.1% for wind, compared to existing algorithms based solely on electricity prices. • Predictive control for a rSOC microgrid considering electricity and hydrogen markets. • The solar energy diurnal cycle mainly determines the operation modes of the rSOC. • Wind-dominated microgrids show higher rSOC usage when considering hydrogen markets. • Considering hydrogen markets boosts microgrid revenue by 4.6% (solar) and 14% (wind). [ABSTRACT FROM AUTHOR]

As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy. [ABSTRACT FROM AUTHOR]

• Treatment of anaerobic digestion digestate has significant climate change impact. • Detailed assessment of the biotreatment of liquid digestate is recommended. • N 2 O emissions from the biotreatment of liquid digestate are substantial. • The impact of the preferred digestate biotreatment changes with the energy system. • The biowaste system is a burden to climate change in a renewable future. The liquid fraction of digestate (LFD) from anaerobic digestion of food waste contains high nitrogen concentrations, and in some countries, the LFD is treated as wastewater. We modelled alternative LFD treatments, including pretreatment with the partial nitritation Anammox (PNA) process. The PNA effluent is discharged to the sewers to undergo further treatment by conventional nitrification and (post- or pre-) denitrification. Life-cycle inventories were developed for the LFD treatment alternatives, including N 2 O emissions and electricity consumption estimates. The climate change (CC) impact was estimated using life cycle assessment in three different energy systems ranging from fossil-based to fully renewable. In the fossil energy system, pretreatment with PNA was attractive, while in the more renewable energy systems, the PNA process did not improve the CC account due to high N 2 O emissions. Pre-denitrification is the most attractive LFD treatment technology in a fully renewable energy system. Linking the LFD treatment to the anaerobic digestion of food waste showed that LFD treatment is a significant contributor to the overall CC account. As we move towards less fossil-based electricity, the anaerobic digestion of food waste constitutes a CC load of 350–450 kg CO 2 -eq/tonne biowaste, of which up to a third can be attributed to the LFD treatment. The N 2 O emissions are the main contributor, constituting up to 50 % in a fossil-based energy system and even higher in a renewable energy system. We conclude that the LFD treatment must be addressed in assessing anaerobic digestion when the LFD is discharged to the sewer. Our study also points to the need to find alternative ways of managing the LFD. [ABSTRACT FROM AUTHOR]

This article considers the system for controlling the diving depth of a drifting autonomous underwater vehicle (DAUV), which navigates underwater under the influence of sea currents in order to collect scientific information. The paper solves the problem of identifying non-stationary hydrodynamic parameters of the DAUV with the aim of adaptive adjustment of the DAUV control algorithm to increase the accuracy of bringing the DAUV to a given depth and minimizing the consumption of electricity consumed by power actuators. The solution to the problem is based on the use of parametric identification apparatus and adaptive control principles. The high quality of the DAUV diving depth control is achieved through the use of the method of adaptive adjustment of the parameters of the DAUV program model. The use of parametric identification of the hydrodynamic parameters of the DAUV made it possible to quickly adjust the corrective link in the control chain of the executing mechanism of the DAUV. The developed computer models and a set of semi-realistic tests made it possible to choose the most acceptable identification algorithm and configure the software implementation of the DAUV diving depth control law. [ABSTRACT FROM AUTHOR]

Despite the extensive body of research on photovoltaic (PV)/thermal systems, a gap remains in evaluating their performance in residential settings. This study aims to bridge this gap by focusing on the energy modeling of a PV/Thermal (PVT) hybrid panel that incorporates heat pipe technology. The evaluation is conducted through MATLAB code to assess the system's capability to fulfill the electricity and heating demands of residential buildings. The model's reliability is affirmed by comparing it with experimental data from a PVT panel tested in Sydney, exploring the transient variations in both water heat gain rates and power generation. The model's precision is evident from the percentage of error in the estimated temperatures of the PV panel based on the test results under various weather conditions, which ranged from -8% to 6%. This method was also utilized to determine the overall energy efficiency of the PVT panel under different climatic conditions. The results reveal that the overall energy efficiency of the proposed PVT panel, on a typical day, is approximately 45%, significantly outperforming traditional PV panels by more than double. Furthermore, the payback period for a typical residential PVT system, providing both hot water and electricity, is found substantially shorter than that of installing separate PV and solar hot water systems, highlighting the economic and environmental benefits of the proposed hybrid system. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the potential for polygeneration to reduce the climate impacts of a coal-based H 2 system via a series of single-impact life-cycle assessments (LCAs) studying the global-warming potential of H 2 -fuel, electricity, and ammonia production. This allows us to determine carbon capture, usage, and storage (CCUS) requirements to match the relevant benchmark emissions and the potential of polygeneration to reduce requirements. We find that implementing polygeneration substantially reduces CCUS energy requirements: while H 2,Th sole production requires very efficient CCUS systems (6% energy penalty) to outperform uncontrolled natural gas, combining polygeneration with a less efficient CCUS (23% penalty) produces low-carbon H 2 and NH 3 (<2.5kg CO2e /kg each) for certain product mixes. Our results show that a system with CCUS with 26% penalty (consuming 400 kWh/TON CO2) can outperform the benchmarks if used to generate electricity at least 50% of the time. This work demonstrates the benefits of flexible LCA approaches in multi-sectoral problems. • We analyze the potential of polygeneration to reduce coal-based H 2 climate impacts. • We develop a tailored LCA with a series of attributional and consequential analyses. • Polygeneration reduces the CCUS needs for coal-based H 2 to outperform natural gas. • Polygeneration can produce low-carbon H 2 with CCUS - even with penalties of 23–36%. • Polygeneration with CCUS produces H 2 and NH 3 with emissions of <2.5 kgCO 2e /kg each. [ABSTRACT FROM AUTHOR]

Liquors can serve as an emergency fuel if they can be directly converted to electricity using a highly efficient solid oxide fuel cell (SOFC). To realize this concept, integrating a paper-structured catalyst (PSC) onto the SOFC anode is an effective solution. Shochu is a most popular distilled liquor in Japan with nominal composition of 25 vol% ethanol and 75 vol% water. In this study, the catalytic performance of a Ni-loaded hydrotalcite (HT) dispersed-PSC (Ni/HT-PSC) toward the steam reforming of ethanol (SRE) was evaluated in 500–800 °C when shochu was supplied. The H 2 production rate for a shochu produced by a continuous distillation (Shochu-25°-A) was comparable to that of a mixture of 25 vol% ethanol–75 vol% water (simulated-Shochu). However, that for a shochu produced by a single-type distillation (Shochu-25°-B) containing various types of volatile organic compounds (VOCs), including sulfur compounds, was lower in the whole investigated temperature range. The results thus indicate that Shochu-25°-A is suitable as an emergency fuel, whereas a refinement process is necessary for Shochu-25°-B. Electrochemical evaluations of electrolyte-supported cells (ESCs) with the Ni/HT-PSC applied on the anode revealed that the PSC effectively avoids coking on the anode when shochu is supplied for power generation at 750 °C. [Display omitted] • Suitability of shochu, poplar liquor in Japan, as a fuel for SOFC was investigated. • Paper-structured catalyst (PSC) was applied for steam reforming of ethanol (SRE). • Shochu with lower VOC content behaved like a mixture of pure ethanol and water. • Shochu produced by single distillation was characterized by a higher VOC content. • Sulfur-containing VOCs must be removed for both reforming and power generation. [ABSTRACT FROM AUTHOR]

Recycling lithium-ion batteries (LIBs) can supplement critical materials and improve the environmental sustainability of LIB supply chains. In this work, environmental impacts (greenhouse gas emissions, water consumption, energy consumption) of industrial-scale production of battery-grade cathode materials from end-of-life LIBs are compared to those of conventional mining supply chains. Converting mixed-stream LIBs into battery-grade materials reduces environmental impacts by at least 58%. Recycling batteries to mixed metal products instead of discrete salts further reduces environmental impacts. Electricity consumption is identified as the principal contributor to all LIB recycling environmental impacts, and different electricity sources can change greenhouse gas emissions up to five times. Supply chain steps that precede refinement (material extraction and transport) contribute marginally to the environmental impacts of circular LIB supply chains (<4%), but are more significant in conventional supply chains (30%). This analysis provides insights for advancing sustainable LIB supply chains, and informs optimization of industrial-scale environmental impacts for emerging battery recycling efforts. Battery recycling LCA shows that recycling can reduce 58% of environmental impacts of making mixed salt solutions compared to conventional mining. Electricity and hydrometallurgical processes dominate impacts and show improvement opportunities. [ABSTRACT FROM AUTHOR]

The demand for fine-grained perception of electricity usage information in the new power system is continuously increasing, making it a challenge to address potential unauthorized data access while ensuring channel security. This paper addresses privacy in power systems requiring efficient source-load interactions by introducing a novel data compression synchronous encryption algorithm within a compressed sensing framework. Our proposed algorithm uses a ternary Logistic-Tent chaotic system for generating a chaotic measurement matrix, allowing simultaneous data compression and encryption of user-side voltage and current data. This mitigates high-frequency sampling overload and ensures data confidentiality. The implementation of a joint random model at both compression and reconstruction stages eliminates the need for key transmission, reducing management costs and leakage risks. The proposed algorithm was validated using the PLAID dataset, demonstrating that the time required for a single encryption-decryption operation can be reduced by up to 81.99% compared to the asymmetric RSA algorithm. Additionally, compared to the symmetric AES algorithm, the proposed method significantly enhances confidentiality. [ABSTRACT FROM AUTHOR]

A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method. [ABSTRACT FROM AUTHOR]

Domestic electricity consumption in the Kingdom of Bahrain accounts for 48% of total national electricity consumption, increasing between 1.5 and 3.5% annually. This increase is due to indoor cooling electricity accounting for up to 80% of domestic electricity consumption. The Kingdom is aiming for a reduction in carbon emissions of 30% by 2035 and to achieve carbon neutrality by 2060. Hence, reducing electricity consumption is necessary. Recently, the Kingdom's Electricity and Water Authority has issued updated building regulations regarding the maximum thermal transmittance allowed for residential buildings. This study employed a quantitative simulation of a typical housing unit (T8) in the Kingdom of Bahrain, assessing building envelope materials and air conditioning efficacy following the updated building regulations via DesignBuilder V. 7.0.2.006 software. Additionally, this study examined the potential of building regulations to facilitate the transition to net-zero energy buildings by comparing electricity consumption with renewable energy generated from rooftop photovoltaic panels. It was determined that electricity consumption could be reduced by up to 52% by following building regulations and relying on current materials in the residential sector. Furthermore, this reduction may facilitate the Kingdom's attainment of net-zero energy status through onsite power generation of 12,500 kWh/year. This study concluded that achieving net-zero energy status is possible by following building regulations and relying on commercially accessible construction materials; however, guidelines for energy storage or a feed-in tariff for the residential sector must be established. [ABSTRACT FROM AUTHOR]

Accurate Short-Term Load Forecasting (STLF) is a critical task in managing and operating smart grids. Existing STLF methods primarily rely on mathematical modeling or neural networks, often struggle to effectively capture the correlations between influencing factors and load data, and frequently lack interpretability. To address these challenges, this paper proposes an intelligent framework for STLF that combines a pattern extraction and attention mechanism, which leverages the characteristics of electricity consumption data. The proposed framework facilitates the integration of prior knowledge, identifies intrinsic data patterns, and more accurately maps the relationships between influencing factors and load patterns. Finally, we conduct experiments on real-world and publicly available datasets to evaluate the performance of the proposed model. Specifically, the proposed model improves the accuracy of STLF compared to that of existing methods and reduces the mean absolute percentage error by 2 % to 5 % . The model performs superiorly on the real datasets, with root mean squared error and mean absolute percentage error values of 0.810 MWh and 7.09 % . [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a methodology for the reliable estimation of the energy consumption of electric vehicles, focusing on the main electrical subsystems of passenger cars. This paper presents a comparative analysis of the available regression models and the results of our simulation experiments. While numerous regression models have been documented in the literature, their accuracy is not always satisfactory. Consequently, there is a need to develop a sufficiently accurate and comprehensive generalized simulation framework, which is presented in the paper. Currently, most of the major vehicle manufacturers have developed pure electric vehicle platforms and are using them in the production of many models available on the market. The estimation of consumption data for these vehicles is still based on traditional techniques, namely, prediction from historical operation data. To overcome this problem, in this article, we have constructed a multi-element, model-based simulation for the purpose of implementing an energy consumption monitoring system. In order to create a simulation that reflects real-life vehicle behavior, the input data are based on empirical measurements, while the simulation model is based on actual electric vehicle parameters. In the main simulation model, it is possible to simulate the energy consumption of the vehicle's drive system and to extract the requisite input data for the simulation of the other vehicle subsystems. In regard to the simulation, the subsystems that have been incorporated are the electric vehicle steering system, the vehicle lighting system and the HVAC system. After running the simulation, the total system consumption for a given trip segment is obtained by running each vehicle subsystem simulation. The findings were validated with real data and compared with two relevant regression models. Our preliminary expectation is that, given the level of detail of our simulation, the developed model can be considered validated if the error of the estimate remains below 4% and if the simulation model in question yields superior results in comparison to other regression models. [ABSTRACT FROM AUTHOR]

Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects. [ABSTRACT FROM AUTHOR]

Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consumption and has potential to improve efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household-related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development. [ABSTRACT FROM AUTHOR]

The very large global demand for energy storage as inherently-variable renewable-energy sources meet an increasing proportion of total electricity demand will be difficult to meet solely with existing technologies. Hence additional storage technologies that are safe and based on abundant primary resources are likely to come into play to facilitate the transition to zero net emissions at the global level. One such promising technology is the 'proton battery', which in its most general form is a rechargeable battery based on proton transfer and reversible electrochemical hydrogen storage. In the present review, a general definition of a proton battery is first proposed, since the term has been used broadly and somewhat inconsistently to date. The literature over the past thirty years on this technology is then critically reviewed, covering both proton batteries that meet the definition proposed in this paper as well as those that are merely self-identified. To the extent possible through published information, the performances of this range of cells are compared in terms of key parameters such as electrical energy stored per unit mass, cyclability, self-discharge and scale reached. The proton battery design developed by our group at RMIT is described in more detail, both theoretically and in terms of experimentally-measured performance, as an exemplar of a system that has already demonstrated a competitive storage capacity at a significant scale. In conclusion, potential future applications for proton batteries, and some directions for the research and development necessary to enable this potential to be realised, are proposed. [ABSTRACT FROM AUTHOR]

This article presents a load forecasting model for commercial buildings with Enhanced Dynamically Weighted Multiple Kernel Support Vector Regression (EDW-MKSVR) and a mini-batch gradient descent method to achieve good regularization along with clustering techniques to segment different times of the day. The first step in preparing the dataset is to perform preprocessing, which involves conducting correlation analysis, scaling, and normalization followed by initial hyperparameter tuning using the multiswarm Levy flight particle swarm optimization technique. Compared with traditional methods, EDW-MKSVR offers greater adaptability to shifting load patterns since it makes use of dynamic kernel weight modifications that are dependent on data attributes. This method is used on the Commercial Buildings Energy Consumption Survey dataset, which includes information on the energy use of commercial buildings together with the climatic characteristics that are related to them across a range of periods, including the week, season, hour, and human behavior. This segmentation can be carried out using the clustering technique. In terms of performance, the study assesses the effectiveness of EDW-MKSVR models against those of boosted tree, random forest regression, K-nearest neighbor, support vector regression, and long short term memory. The study's conclusions imply that EDW-MKSVR outperforms other current methods in terms of accuracy when obtaining complex load patterns. This article demonstrates how reliable and accurate the EDW-MKSVR approach is in predicting energy use. It facilitates the power industry's ability to make better judgments by providing more precision and flexibility. Furthermore, the robustness of the proposed model is evaluated by predicting the electricity consumption of two additional datasets using the proposed method. Next, we compare these predictions' performance measures, which has demonstrated the ability to generate highly ranked accuracy metrics. [ABSTRACT FROM AUTHOR]

This study explores the improvement of building integrated photovoltaic–thermal (BIPV/T) systems and their integration with air source heat pumps (ASHPs). The BIPV/T collector needs a method to effectively extract the heat it collects, while ASHP can boost their efficiency utilizing preheated air from the BIPV/T collectors. Combining these two systems presents a valuable opportunity to enhance their performance. This paper discusses technological improvements and integration through a comprehensive modelling analysis. Two versions of the BIPV/T systems were assessed using a modified version of EnergyPlus V8.0, a building energy simulation program. This study involved sensitivity analysis of the internal channel surface and cover emissivity parameters of the opaque BIPV/T (OBIPV/T), transparent BIPV/T (TBIPV/T), and building-integrated solar air heater collectors (BISAHs). Various arrangements of the collectors were also studied. A BIPV/T-BISAH array design was selected based on the analysis, and its integration with a net-zero energy house. The BIPV/T-BISAH coupled ASHP system decreased space heating electricity consumption by 6.5% for a net-zero house. These modest savings are mainly attributed to the passive design of the houses, which reduced heating loads during sunny hours/days. [ABSTRACT FROM AUTHOR]

The increasing share of renewable energies within energy systems leads to an increase in complexity. The growing complexity is due to the diversity of technologies, ongoing technological innovations, and fluctuating electricity production. To continue to ensure a secure, economical, and needs-based energy supply, additional information is needed to efficiently control these systems. This impacts public and industrial supply systems, such as vehicle factories. This paper examines the influencing factors and the applicability of the Temporal Fusion Transformer (TFT) model for the weekly energy demand forecast at an automobile production site. Seven different TFT models were trained for the weekly forecast of energy demand. Six models predicted the energy demand for electricity, heat, and natural gas. Three models used a rolling day-ahead forecast, and three models predicted the entire week in one step. In the seventh model, the rolling day-ahead forecast was used again, with the three target values being predicted in the same model. The analysis of the models shows that the rolling day-ahead forecasting method with a MAPE of 13% already delivers good results in predicting the electrical energy demand. The prediction accuracy achieved is sufficient to use the model outcomes as a basis for weekly operational planning and energy demand reporting. However, further improvements are still required for use in automated control of the energy system to reduce energy procurement costs. The models for forecasting heat and natural gas demands still show too high deviations, with a MAPE of 62% for heat demand and a MAPE of 39% for natural gas demand. To accurately predict these demands, further factors must be identified to explain the demand. [ABSTRACT FROM AUTHOR]

Power consumption management is crucial to maintaining the reliable operation of power grids, especially in the context of the decarbonization of the electric power industry. Managing power consumption of industrial enterprises by personnel proved ineffective, which required the development and implementation of automatic energy consumption management systems. Optimization of power consumption behavior requires comprehensive and reliable information on the parameters of the technological processes of an industrial enterprise. The paper explores the specific features of non-stationary conditions of output production and assesses the potential for power consumption management under these conditions. The analysis of power consumption modes was carried out based on the consideration of random factors determined by both internal and external circumstances, subject to the fulfillment of the production plan. This made it possible to increase the efficiency of power consumption in mechanical engineering production by taking into account the uncertainty of seasonal and technological fluctuations by 15–20%, subject to the fulfillment of the production plan. This study presents a justification for utilizing the theory of level-crossings of random processes to enhance the reliability of input information. The need to analyze the specific features of technological processes based on the probabilistic structure and random functions is proven. This is justified because it becomes possible to fulfill the production plan with technological fluctuations in productivity and, accordingly, power consumption, which exceeds the nominal values by more than 5%. In addition, the emission characteristics are clear, easy to measure, and allow the transition from analog to digital information presentation. The algorithm and methods developed to analyze the power consumption patterns of industrial enterprises can be used to develop automatic power consumption management systems. [ABSTRACT FROM AUTHOR]

Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes' constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques. [ABSTRACT FROM AUTHOR]

The research on electricity consumption pattern recognition generally encounters some prominent problems such as poor similarity, poor accuracy, and low efficiency of existing clustering algorithms. Therefore, this paper utilizes elbow judgment (EJ), gap statistic (GS), and DTW (dynamic time warping) to develop a DTW-based adaptive K-means (DAKM) clustering algorithm for electricity consumption pattern recognition. The algorithm includes three main aspects. First, the DTW distance with the Sakoe-Chiba band global constraint is used to find the optimal alignment between the two load curves by matching the shapes with local stretching or compression sequences. Second, gap statistic and elbow are used to obtain the optimal number of clusters for high clustering efficiency automatically. Third, a max-min DTW distance (MMDD) method is presented to optimize the initial cluster centers of the K-means algorithm. The comparative experimental results demonstrate that the proposed DAKM algorithm achieved best evaluation values of 0.7055 for DBI, 0.0237 for SSE, 132.0435 for CHI, 0.6649 for SC, and 1.1670 for DI, respectively, which proves that the proposed DAKM algorithm is far superior to other clustering algorithms. [ABSTRACT FROM AUTHOR]

In the present time, electricity stands as one of the most fundamental needs within human societies. This is evident in the fact that all industrial activities and a significant portion of social, economic, agricultural, and other activities rely heavily on this energy source. As a result, both the quality and continuity of electricity hold immense importance. The primary objective of this study is to predict short-term changes in load consumption. These predictions are based on a range of factors that influence electric consumption, factors characterized by complex nonlinear relationships. Notably, these factors encompass climate shifts and fluctuations within daily consumption cycles. The proposed method for short-term load forecasting (STLF) involves a hybrid neural network utilizing deep learning techniques. Specifically, it combines the convolutional neural network (CNN) and long short-term memory (LSTM) architectures. The CNN architecture is leveraged for its proficiency in extracting patterns from data, while the LSTM architecture excels in predicting time series data. The suggested approach enables the anticipation of future consumption patterns by considering upcoming weather conditions and analyzing past electricity consumption trends. Numerical results underscore the enhanced forecast precision achieved through this method, which is about 1% better than the best previous results, as evidenced by improvements in metrics such as the root mean square error (RMSE) and the mean absolute percentage error (MAPE). These improvements outperform the best available methods presented in prior research. Thus, this paper not only contributes a novel approach but also serves as a comprehensive review of the latest developments in the realm of short-term load forecasting. [ABSTRACT FROM AUTHOR]

The control and industrial application of chaotic systems is a major obstacle limiting the diffusion of chaos theory. In this study, we proposed a novel, universally applicable methodology for constructing an offset boosting function for chaotic systems. By integrating this approach with traditional techniques, a four-dimensional chaotic system with two-dimensional offset boosting was developed and successfully implemented by a real chaotic circuit for manganese metal electrolysis, replacing conventional DC. It has been shown that the use of time-varying electricity facilitates the suppression of electrochemical oscillations, and inhibits the growth of spherical manganese nodules. An examination of current efficiency for different oscillations (period 1, period 2, chaos-a and chaos-b) and various current amplitudes has established that chaos-a electrical signals are most suitable for inhibiting the growth of manganese nodules. The Mn nodule area ratios can be reduced by 38% with a 5.83% increase in current efficiency, representing an energy consumption saving of 0.58 kWh/kg. This interdisciplinary approach holds promise for advancing the study of nonlinear dynamic behavior in electrochemical reaction processes and addressing critical challenges in various fields, such as energy dissipation, quality improvement of electrodeposited products, and regulation of by-product properties. [ABSTRACT FROM AUTHOR]

The renewable energy transition requires more flexible electricity consumption. This article follows up on Norwegian plans to achieve this through demand response and a piloting of smart home technology targeting indoor heating, which we call Smart Energy Technology (SET). Based on 17 in-depth interviews with participants in a technology pilot project, we map the work required to keep the SET system running, how it impacted electricity-consuming household practices, and analyse why use of the system varied widely among participants. We show that the system in question was too complex for other than exceptionally skilled and motivated users, who engaged in extensive "digital housekeeping". Other users were navigating a complex Norwegian electricity market using technology they found difficult to operate. This was linked with limited system use, which lowered the energy efficiency contribution of the smart system. Smart systems and their use are highly gendered and have the potential both to challenge and reinforce gendered divisions of labour. More research into the gendered impacts of smart systems in Norway is needed. Simpler and more user-friendly systems are necessary for future pilot projects, more hands-on training for users in such pilots is required, and the daily work required to operate complex smart systems should be recognised more clearly by both smart system developers and policy makers. [ABSTRACT FROM AUTHOR]

• Monitoring the energy performance separately of air handling components results more real scientific conclusions on the energy consumption of a ventilation system. • Monitoring the CO 2 cross-contamination in the heat wheel shows the real operation properties of the heat wheel. • The effectiveness of the heat wheel is evaluated as 51 % under real operation conditions based on the monitored data in the investigated heating period, which 23.8 % less than the data (74.9 %) given in the technical data book of the manufacturer. • Despite the lower but real effectiveness value written above, the operation of an air-to-air rotary heat recovery unit result significant 20.5 % energy saving rate on the electric power consumption of the variable refrigerant volume (VRV) outdoor unit. • Heating season has less impact on CO 2 cross-contamination from the exhaust air flow to the supply air flow in the heat wheel. This study presents an on-site examination of the heating energy performance of a real ventilation system, running on the flat roof of a shopping complex building in Hungary. The main focus of this study is to delve into the heating energy efficiency of specific air handling elements, such as the air-to-air rotary heat wheel and the direct expansion heating coil supplied by a variable refrigerant volume outdoor unit under real operation conditions. Additionally, an investigation was carried out during the heating season to scrutinize the potential occurrence of CO 2 transfer from the exhaust airflow to the supply airflow within the heat wheel, which issue highlights a notable limitation of the heat recovery system. Based on the monitored and measured data, the energy saving impact of the heat wheel was 20.5 % on the electric power consumption of the variable refrigerant volume outdoor unit throughout the entire heating period, compared to operation without heat wheel. Furthermore, the relative average of CO 2 cross-contamination is recorded as 3.8 % in the investigated heating season. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to tackle Egypt's rising electricity consumption due to climate change and population growth, focusing on the building sector, which accounts for up to 60% of the issue, by developing new energy-efficient design guidelines for Egyptian buildings. Design/methodology/approach: This study comprises six key steps. A literature review focuses on energy consumption and efficiency in buildings, monitoring a single-family building in Cairo, using Energy Plus for simulation and verification, performing multi-objective optimization, comparing energy performance between base and controlled cases, and developing a localized version of the Passive House (PH) called Energy Efficiency Design Criteria (EEDC). Findings: The research shows that applying the (EEDC) suggested by this study can decrease energy consumption by up to 58% and decrease cooling consumption by up to 63% in residential buildings in Egypt while providing thermal comfort and reducing greenhouse gas emissions. This can benefit users, alleviate local power grid strain, contribute to Egypt's economy, and serve as a model for other countries with similar climates. Originality/value: To date, no studies have focused on developing energy-efficient design standards tailored to the Egyptian climate and context using the Passive House Criteria concept. This study contributes to the field by identifying key principles, design details, and goal requirements needed to promote energy-efficient design standards for residential buildings in Egypt. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The insurance industry, as one of the important sectors in the global economy, has a role in addressing environmental challenges through initiatives such as green information technology. Green IT in insurance refers to the adoption of environmentally friendly practices and technologies in the insurance industry in order to reduce carbon footprints and contribute to sustainable efforts. The purpose of this research is to present the framework for the implementation of green information technology in insurance as a guideline for the successful implementation of green information technology and to determine the actions of insurance companies to implement green information technology and create a sustainable environment. METHODS: The research method is hybrid or combined (literature review, work in the field and final analysis). The qualitative method of this research is based on a systematic review. First, the basic framework of information technology implementation was obtained using content analysis of articles. Then, to present the final framework specific to the insurance industry, an interview was conducted with insurance industry experts. After that, the obtained framework was validated by the experts by sending a questionnaire and calculating the CVR coefficient of Lawshe. FINDINGS: The findings reveal the necessity of ecological sustainability as a reality. The result of this study contains 34 categories and 8 major dimensions, including implementation drivers, resources and organizational capabilities for green IT implementation, standards and criteria, strategy, green application (use), green scrapping and the consequences of green IT implementation. And it shows the responsible actions of insurance companies to reduce pollution and protect the environment. CONCLUSION: The results showed that the most common factor in the implementation of green information technology is the reduction of electricity consumption, the social responsibility of the company towards the environment, and the existence of organizational and government rules and regulations in insurance companies. The findings of this research have addressed the research problem by developing the implementation framework of green information technology in the insurance industry. [ABSTRACT FROM AUTHOR]

In this proposed work, two prototypes are implemented, one is for smart parking and the other is for monitoring the energy consumption of the same. With the implementation of IoT based Street Light System in the field, nearly 60-70 percent of electricity saving can be achieved as compared to conventional systems. Here, this system is not only controlling the switching of the street lights ON/OFF but these electricity consumption details can be monitored remotely through another developed prototype. It is demonstrated that for a single pole electricity consumption is nearly 1.2KW load per pole which results in power consumption of 14.4KWh from evening 6:00 PM to morning 6:00AM with conventional system. The implemented system is energy efficient in terms of energy saving which is nearly 9.6KWh can be achieved per day on each pole with same specifications. Moreover, real-time implementation of the proposed system is also demonstrated. IoT is a key technology in healthcare sector for managing, monitoring and controlling the medical devices, services and processes with the basic sensing and actuating components. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to provide empirical evidence on two research questions: firstly, whether green finance is positively related to electricity access, and, secondly, if the domestic economic environment moderates the relationship between green finance and electricity access? This paper pays particular attention to the regional disparities in Africa. Design/methodology/approach: While pursuing the study objectives, the authors apply a variety of statistical approaches and tools to assess the robustness of the findings. The authors use panel dataset for analysing data. In order to empirically examine the relationship between green finance and electricity access in the African region, the paper employs static and dynamic panel estimation methods, Poisson method and adopts two-step system generalized method of moments (GMM) approach for dealing with issues relating to endogeneity. The authors also use alternate proxy for the electricity access, which is drawn from the regulatory indicators for sustainable energy (RISE) scores. Findings: The authors find that despite the fact that green funding appears to support job creation, household incomes aren't high enough to drive rising demand for electricity. The study underscores the role and responsibilities of external funding agencies to ensure that funds at the receiving end are effectively routed to encourage access to clean and sustainable energy, which is good to the economic and domestic environment. Further, due to the relatively modest size of some funds, the cost to administer those funds is larger than the funds themselves. This causes inefficiencies, which may temporarily provide jobs but not lasting growth. This means there is no regular need for energy, therefore larger investors have no reason to enter the market. This discourages investors from public-private partnerships or private investments and prevents future investment. Research limitations/implications: The provide insights into the private-public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries. Practical implications: The study confirms the view that trivial amounts of green financing (US-Aid or grants) impose a burden on the absorptive capacity of the recipient government and increases the transaction costs and is likely to be an impediment (Kimura et al., 2012) to initiating projects that enhance electricity access. Social implications: The results indicate that although green financing seems to be supporting employment opportunities, income levels are insufficient to create demand for electricity usage. It, therefore, becomes imperative that sanitation (SDG 6) is fully addressed in order to ensure that SDG 7 is attained. Originality/value: The authors provide insights around the private public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries. [ABSTRACT FROM AUTHOR]

Attention and efforts to reduce greenhouse gases including carbon dioxide is increasing, and begun to be socialized at household level. This study aims to calculate total carbon emissions from household activities that use energy, for evaluation the carbon absorption. The research method was a qualitative survey using questionnaires and field observations from 380 residents spread over 7 villages in Selong sub-district, East Lombok Regency, West Nusa Tenggara Province. Complementary data obtained from literature studies regarding the provisions and rules used. The findings show that indirect emissions originating from electricity use dominate with 69% (481.91638 ton CO2/year), which shows very high dependency. The general type of settlement was a mixture of residential and economic activities, which easy to find small shops, service kiosks such as laundry, computer and internet rental, grocery, rented and boarding houses that causes high demand for electricity. Electrical energy comes from diesel power plants that still use coal and other fossil fuels in the production process. Evaluation of emission absorption shows unbalance. It is necessary to save energy usage and also make efforts to find and utilize renewable energy sources and designing scenarios to optimize open spaces, both by increasing the number and model themselves. [ABSTRACT FROM AUTHOR]

With the expansion of public buildings in China and rise in carbon emission from energy consumption, it has become essential to improve the efficiency of public building operation energy consumption to reduce carbon emissions. However, it is not clear how to optimally reduce the energy consumption of public buildings from a holistic perspective. This study identifies the factors impacting energy consumption of public buildings by reviewing existing literature and establishes a framework for illustrating the interactions between the identified factors. The framework is validated through a case study of a university building in Beijing by methods of direct observation, instrument measurement, scrutiny of the building's operation records, simulating, and fitting. The framework demonstrates the interactions among the building energy consumption factors, which include social and economic factors, technology factors, building factors, environmental factors, occupant behavior patterns, and energy consumption equipment. The results of the study reveal the operation energy consumption of the case study university building regarding electricity consumption, energy utilization intensity, illumination of the building, and occupant behavior. It was found that indoor illumination intensities are affected by WWR, orientation of the room, and distance from the window, and that there is a functional relationship between illumination intensity and distance from the window. The outcomes of the study have significant contributions to the body of knowledge on energy consumption of public buildings. Theoretically, the proposed framework can help with understanding the relationships among the different factors affecting the energy consumption of public building operations. Practically, the measures arising from the study's empirical evidence, which include using a building automation system, installing solar panels with a potential to generate 31.6% of the building's total consumed energy, and improving occupants' habits with impact of saving 18.8% of the total energy consumption of classrooms, can help optimize the operation of public buildings for improving energy consumption efficiency. [ABSTRACT FROM AUTHOR]

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The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year's operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features. [ABSTRACT FROM AUTHOR]

It is commonly known that buildings in hot climate contribute to a huge amount of electricity consumption mainly due to air conditioning needs. Many countries around the world are aiming to convert buildings to net zero energy buildings (NZEB). However, buildings in hot climates require varieties of active and passive measures to adapt the concepts behind NZEB. This work attempts to resolve the challenges associated with shifting school buildings to NZEB in hot arid climates. It presents an energy performance analysis that is focused on two scenarios for new and retrofitted schools. Building thermal simulation is used to assess the implications of several energy conservation measures, and different scenarios are suggested to utilize up to 80% of roof's area for the installation of Photovoltaics (PV), and on-site wind turbines. The implemented energy conservation measures show a reduction in annual energy consumption by 35% and 21% for new and retrofitted schools respectively. Discounted payback period is used to estimate the economic feasibility of the suggested scenarios. It is found that NZEB is technically feasible at highest roof area PV installations with respective discounted paybacks of 3.55 and 5.54 years for the new and retrofitted schools. However, adding wind-turbines can delay the breakeven year of investments needed to achieve NZEB. The estimated savings in net present value (NPV) are 3273 and 4284 thousand US dollars for the retrofitted and new schools respectively, and each school's roof can generate 40.63 GWh in 25 years and avoid 29.23 kilotons of CO2. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic (RTPV) systems have the potential to significantly boost residential electricity self-sufficiency in urban areas. However, estimating the self-sufficiency potential of each city is challenging due to the trade-off between target accuracy and data availability, which limits the scalability of existing methods. This study aims to evaluate the potential of RTPV systems to enhance residential electricity self-sufficiency in major Japanese cities. The self-sufficiency analysis employs a balanced approach using statistical data to estimate RTPV and battery storage capacity in detached houses and hourly simulations to capture supply–demand variations. To project the penetration rate, a logistic curve is utilized to estimate the timeline for achieving a 100% installation rate in detached houses. The analysis reveals that RTPV systems could supply approximately 40% of the residential electricity demand in major cities, with some achieving self-sufficiency rates exceeding 65%. Densely populated cities like Tokyo, Osaka, and Kawasaki may only meet a quarter of their demand due to higher energy requirements. Including older detached houses in RTPV deployment boosted self-sufficiency by an average of 11.77%, with cities like Nagoya, Kyoto, and Kitakyushu achieving increases of 15–20%. Battery storage plays a critical role in enhancing self-sufficiency and reducing energy curtailment. Logistic curve projections suggest that most cities are unlikely to reach 100% RTPV penetration before 2050, though leading cities could achieve 75% penetration by then due to favorable growth rates. These findings reveal that while RTPV has substantial potential to improve residential electricity self-sufficiency, additional efforts are necessary to accelerate adoption. Further research is needed to refine capacity estimates, explore the socioeconomic and political context of the cities, and examine alternative pathways for cities like Tokyo, Osaka, and Kawasaki. [ABSTRACT FROM AUTHOR]