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The purpose of long-term forecasting is to meet the needs of practical applications, such as the prediction of the development trend of infectious diseases and the planning of electricity consumption. In this paper, we study the long-term forecasting of time series. Studies have shown that previous Transformer-based models have the potential to improve prediction capabilities, but there are also some problems, such as the lack of location information and the slow training speed. To solve these problems, we designed an efficient Transformer-based model called AD-Autoformer, which is specifically designed for long-term series prediction. By introducing position embedding, the model can better understand the patterns and relationships in the sequence, to improve the performance and generalization ability of the model when processing sequence data, and the self-attention distilling mechanism realizes the compression and acceleration of the model by halving the cascading layer input to highlight the dominant attention. This method significantly reduces the computational complexity of the model and improves the training speed of the model while maintaining the performance of the model. Experimental results on five large datasets show that the proposed AD-Autoformer model has different degrees of improvement in MSE and MAE indicators compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

Intersecting socio-demographic transformations and warming climates portend increasing worldwide heat exposures and health sequelae. Cooling adaptation via air conditioning (AC) is effective, but energy-intensive and constrained by household-level differences in income and adaptive capacity. Using statistical models trained on a large multi-country household survey dataset (n = 673,215), we project AC adoption and energy use to mid-century at fine spatial resolution worldwide. Globally, the share of households with residential AC could grow from 27% to 41% (range of scenarios assessed: 33-48%), implying up to a doubling of residential cooling electricity consumption, from 1220 to 1940 (scenarios range: 1590-2377) terawatt-hours yr.−1, emitting between 590 and 1,365 million tons of carbon dioxide equivalent (MtCO2e). AC access and utilization will remain highly unequal within and across countries and income groups, with significant regressive impacts. Up to 4 billion people may lack air-conditioning in 2050. Our global gridded projections facilitate incorporation of AC's vulnerability, health, and decarbonization effects into integrated assessments of climate change. One quarter of households globally owns air-conditioning, but this could reach 41% by 2050. Yet, access and use will remain highly unequal. Electricity demand for air-conditioning will double, potentially increasing global CO2 emissions. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G's security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G's potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification. [ABSTRACT FROM AUTHOR]

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

The instability of the increasingly important photovoltaic (PV) electricity generation in Korea due to a dependence on meteorological conditions is an emerging problem. In this study, we examined the influence of various meteorological conditions on the prediction accuracy of PV generation and module temperature. The predictions were based on weather data acquired from the Korea Meteorological Administration and compared with actual PV generation data from the Dangjin coal yard PV power facility. The results were analyzed on the basis of meteorological conditions, insolation, ambient temperature, and wind velocity. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) is a promising technology that combines communication and data networking. Integration of Smart Grids (SGs) and IoT will fulfill an increased demand for energy requirements by transforming the reliable and efficient traditional power grids. The SG enables bi-directional transmission between the Service Provider (SP) and Smart Meter (SM) to send and receive information regarding electricity consumption over a public channel. The public channel allows an adversary to intercept the information exchanged between them and tamper with the SM as it is installed outside which leads to forging or modification of the messages and privacy violation. In addition, the SM has limited computational and storage capacity. To protect SM privacy and securely communicate in the SG network, Physically Unclonable Functions (PUFs) based Authentication and Key Agreement (AKA) schemes were suggested in the literature. However, they may suffer from the machine learning modeling attack and several other security issues. Thus, we propose a finite state machine enabled controlled PUF based AKA (PF-AKA) Framework for the IoT based SG (IoT-SG) network. The PF-AKA framework is verified formally using the Real-or-Random (RoR) model, AVISPA tool, and BAN logic. It shows that PF-AKA achieves the security requirements along with protection from the SM physical and modeling attacks. The performance analysis is carried out and it represents that the PF-AKA yields competitive computation and communication costs compared to AKA schemes in the literature for the IoT-SG network. [ABSTRACT FROM AUTHOR]

A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]

Solar photovoltaics have emerged as an economic source of renewable electricity generation for regions with high solar irradiation. Still, the installation of utility-scale PV systems is often disrupted due to the unavailability of land. Sparsely populated areas with a hot, dry climate can mitigate the land-energy conflict as they experience higher solar irradiation. In arid areas, the PV systems incur more losses due to dust deposition on PV modules and reduced energy conversion efficiency at higher temperatures. The shortage of water makes the module cleaning procedures cost-prohibitive. Floating Solar photovoltaic (FSPV) systems resolve these problems due to their reduced land footprint, higher efficiency, and reduce evaporative water loss. This study estimates the energy generation of a 4.8 MW FSPV-based microgrid in a hot, semi-arid urban area. By utilizing 5% area of the waterbody, the FSPV system yields 8.876 GWh of electricity annually, to meet 41.7% electricity demand of the study area. The system generates 16.7% more power compared to the land-based PV system. The performance ratio of 82%, and a DC capacity utilization factor of 20.3% is achieved. The system saves 52,810.32 m3 of water per year, compensating for the water requirement of 786.99 m3. for cleaning and dust removal of PV modules. The weekly cleaning procedure limits the soiling loss to 2.79%, compared to 8.47% after a month without cleaning. The FSPV-based microgrid reduces annual CO2 emissions by 2.93 tonnes. This study shall enable engineers, urban planners, and policymakers to develop clean energy-based, sustainable microgrid solutions for urban areas. [ABSTRACT FROM AUTHOR]

The transformation towards cleaner energy consumption in Togo is progressing at a slow pace due to a combination of unfavourable socioeconomic, demographic, and spatial factors that favour traditional fuel use over clean and efficient energy. This study tries to quantify the inequality in modern energy access by applying the inequality of opportunity framework. We use a parametric approach to the 2015 Togolese Living Standard Survey; our results show that, on average, 40.76% of inequalities in household modern energy access and consumption in Togo are due to unequal circumstances beyond their control. A Shapley-value decomposition shows that inequalities of opportunity are greater for older persons and women than for youths and men, respectively. Moreover, these inequalities are more pronounced in rural areas and poor regions, especially the Savanna and Kara regions. Consequently, policies aimed at reducing inequality of opportunity in modern energy access and consumption in Togo should emphasise demographic factors, such as gender, age composition, and geographic location of households. [ABSTRACT FROM AUTHOR]

The electrocatalytic conversion of CO2 to produce fuels and chemicals holds great promise, not only to provide an alternative to fossil feedstocks, but also to use renewable electricity to convert and recycle the greenhouse gas CO2 to mitigate climate problems. However, the selectivity and reaction rates for the conversion of CO2 into desirable carbon-based products, especially multicarbon products with high added value, are still insufficient for commercial applications, which is attributed to insufficiently favourable microenvironmental conditions in the vicinity of the catalyst. The construction of catalysts/electrodes with confined structures can effectively improve the reaction microenvironment in the vicinity of the electrodes and thus effectively direct the reaction towards the desired pathway. In this review, we firstly introduce the effects of the microenvironment at the electrode-electrolyte interface including local pH, local intermediate concentration, and local cation concentration on CO2 reduction reaction (CO2RR) as well as the mechanism of action, and then shed light on the microenvironmental modulation within the confined space, and finally and most importantly, introduce the design strategy of CO2RR catalyst/electrode based on the confinement effect. [ABSTRACT FROM AUTHOR]

Purpose: Cellobiose lipids (CL) are biosurfactants produced by various Ustilaginaceae species in aerobic fermentations. They show high potential for application as alternatives to conventional oleochemical- or petrochemical surfactants. To ensure their environmentally friendly performance, we aimed to assess CL production from a life cycle perspective at an early developmental stage to identify process steps that have the highest impact on the environment. With this information, optimisation approaches can be derived. Materials and methods: Following a cradle-to-gate approach, we modelled the CL fermentation and purification process based on experimental data from the lab scale and process simulation data at a 10 m3 scale. For LCA, the impact categories (IC) abiotic depletion potential (ADP), eutrophication potential, photochemical ozone creation potential, global warming potential, acidification potential, and the primary energy demand were calculated for all process steps. Based on the obtained results, process bottlenecks were identified, and alternative process scenarios varying the related process parameters were simulated. These were used to assess the environmental impact reduction potential (EIRP) of an optimised process and draw recommendations for experimental process optimisation. Results and discussion: The obtained results showed that the fermentation caused ~ 73% of ADP and more than 85% of all other ICs. The major contributor was the electricity consumption for continuous fermenter aeration. Thus, reducing the fermentation duration from the initial 14 to 5 days would result in a decrease in all investigated ICs of up to ~ 27–52%. An increase in CL concentration results in a decrease in all ICs of a similar magnitude due to the higher yield per batch at comparable energy and material consumption. Although the share of purification process steps to all ICs is overall relatively small, implementing foam fractionation for in situ product recovery showed an additional EIRP of 18–27% in all purification IC shares. Conclusions: The conducted LCA showed that overall, more EIRP can be achieved by optimising fermentation process parameters compared to purification process steps. This is mainly due to the long fermentation duration and large energy consumption for fermenter aeration. This highlights the importance of using LCA as a driver for process optimisation to identify process steps with high EIRP. While some of the results are specific to CL, other obtained results can be transferred to other fermentations. [ABSTRACT FROM AUTHOR]

Over the last two decades, research increasingly has paid attention to resilience as a way to strengthen electricity systems against the cascading impacts caused by electricity disruptions. Although much of the electricity resilience literature has focused on scale of large grids, a growing segment of research has focused on smaller-scale electricity systems, particularly with applications for communities. Research on financing these systems could encourage their uptake in local communities, particularly by including community in the ownership or operation of these systems; however, much of this research remains comparatively nascent. This paper seeks to review what previous studies have identified as some of the conditions that shape financing electricity resilience in local communities in G7 countries and how this field uses the term "electricity resilience" compared to broader uses of electricity resilience. The review provides a technical overview of smaller-scale systems for communities and a review of three socio-economic research areas—governance, cost-benefits, and business models—which shape financing electricity resilience in local communities. The discussion section finds that costs and the level of community involvement seem to play a fundamental role in shaping the conditions for financing electricity resilience across much of the research. Comparing this field to broader uses of "electricity resilience" suggests that more work is needed to understand the role of adaptation in financing electricity resilience for local communities, particularly over the long term. We posit that the field's approach costs and its inclusion of the community in electricity resilience may contribute to its general lack of attention to long-run adaptation. Despite potential benefits of continued advancements from technical research, the maturity of the field and age of some of the early cases suggests that researchers could begin to study adaptation to electricity disruptions at the community level more than in the past. [ABSTRACT FROM AUTHOR]

Despite the abundance of research on reducing carbon emissions, there is a significant gap in understanding the influence of macroeconomic factors on carbon dioxide (CO2) emissions from a spatial-structural perspective. This study aims to contribute to the literature by investigating the impact of macroeconomic factors on carbon dioxide emissions in six East African countries between 1989 and 2020. Using spatial econometric panel models, the study analyzed spatial dependence among the variables. The empirical findings indicate that gross domestic product (GDP) per capita and electricity consumption have positive direct and indirect effects on carbon emissions, while fuel prices and exports have negative direct effects, but positive spillover effects on neighboring countries. Imports have a positive impact on local economies, but negative spillover effects. Additionally, the urban population has no significant impact on the environment. These findings provide important policy implications for optimizing spatial growth patterns and achieving a low-carbon economy in East African countries. [ABSTRACT FROM AUTHOR]

Background: There is a close link between the lack of electricity access and poverty indicators such as illiteracy, high infant mortality, lack of access to health care and malnutrition among others. Most rural farming communities in Ghana lack access to electricity due to the high cost of extending the grid to these communities. This lack of access tends to worsen the gap between urban and rural inhabitants regarding access to education, healthcare and development. Methods: This study assessed the technical and theoretical potential of agricultural residues in providing electricity to off-grid communities. The study used crop production figures of maize, cassava, millet and groundnut in the Soma and Goyiri farming communities in the Sawla-Tuna-Kalba District to conduct an assessment of the theoretical and technical potential of residues from the crops. The production figures of these crops were obtained from the District Office of the Ministry of Food and Agriculture. Expected electricity demand of households, schools and health centers in the study communities were collected and employed for the projected load demand estimates. Results: The study found that 312.23 MWh/day of electricity could be generated from the combined residues of maize, cassava, millet and groundnut from the two communities. This amount of electricity is capable of providing ~ 202 to 263 times the peak electricity demand of the studied communities. Out of the total electricity demand of the two communities, only about 91 kWh/day is needed for use in a school and Community Health Promotion and Services (CHPS) compound, implying that the electricity from crop residues can also help to improve education and health provision in the rural communities. Conclusion: It is concluded that the potential of crop residues in meeting the electricity demand of off-grid communities is enormous. Hence, it must be considered in Ghana's energy development plans to achieve universal electricity access. [ABSTRACT FROM AUTHOR]

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method's accuracy showed significant improvements, with determination coefficients (R2) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies. [ABSTRACT FROM AUTHOR]

Electrical energy saving was evaluated by taking advantage of PV and ESS in a community unit. An artificial neural network (ANN) and long short-term memory (LSTM) were employed to create a predictive model for PV generation. Annual demand data for residential buildings were estimated using EnergyPlus, while data for other buildings were collected from measurements in J Energy Town, Republic of Korea. Pearson correlation coefficients identified six crucial variables for the model. Comparative analysis of 310 cases revealed that the best-performing model was an ANN with three hidden layers and nodes of 14, 13 and 11. The model satisfied ASHRAE guidelines with a CV(RMSE) of 29.1 % and NMBE of −7.14 %. Evaluating electricity consumption in the community, case B (PV generation) showed a significant 46.3 % reduction compared to case A, while case D achieved a 5 % energy savings relative to case E over the year. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, traditional local methods usually make predictions based on load aggregation or clustering, the complexity of which will rise greatly with the increase of agent types. It has become more and more difficult for traditional methods to meet the STLF demand of smart grid. Meanwhile, global time series forecasting method emerges gradually in many fields, which can make predictions for many agents only by one model with much lower complexity. However, there is few researches on the multi-agent global STLF. This study proposes a global deep-Long Short-Term Memory (LSTM) STLF model based on a pretraining method. The model fully applies the information such as electricity consumption pattern, weather and calendar in a structural and orderly way. The empirical results show that our model can effectively predict the daily load of heterogeneous households, with prediction accuracies of 87.9–90.2% on the test set across various tasks. Compared with the base model, our model achieves an 8.7% higher accuracy with a much faster convergence speed. Furthermore, the fluctuations of accuracy on different tasks are within 2.3%, showing that our model is robust. The superiority of the global method is proved through the comparison with local method in the empirical results. This study is expected to make contribution to global STLF of heterogeneous households and providing experience for the global prediction of heterogeneous time series based on deep-LSTM and pretraining method. [ABSTRACT FROM AUTHOR]

Forecasting of long-term annual electricity demand is studied utilizing historical data for electrical energy consumption and socio-economic indicators—gross domestic product, population, import and export values for the case of Turkey between 1975 and 2020. A quadratic model for electrical energy consumption was applied to define the relation between the historical and predicted data. This model used metaheuristic algorithms; genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), artificial intelligence (AI) approaches; neural networks (NN), and adaptive network fuzzy inference systems (ANFIS), and machine learning (ML) applications; all models undergo testing, but the top four models—stepwise linear regression (SLR), NN, Gaussian process regression (GPR) with exponential, and GPR with squared exponential—are selected for additional research to determine the best forecasting model based on their forecasting performance. Comparing the finalized models SLR produced the best forecasting model with a mean absolute percentage error (MAPE) value of 2.36%, followed by GA with 2.97%. Turkey's yearly electrical energy consumption is projected under three possible scenarios through 2030. Finding the most appropriate forecasting model among the models studied for long-term electrical energy forecasting is ultimately the primary goal of this research. Simulations are done on the MATLAB™ platform. [ABSTRACT FROM AUTHOR]

This paper introduces and underscores the automation of the E-vehicle charging process through optimization between the grid and the vehicle, incorporating real-time constraints in the charging and discharging processes. With the surge in E-vehicle usage and heightened electricity demand, automating the vehicle battery charging process becomes imperative to mitigate its impact on the grid. The research outlined in this paper focuses on automating electric vehicle charging and discharging based on real-time power demand considerations. The automation process, facilitated through a connection to the power grid, is managed by the interconnected network and algorithm-based monitoring of real-time parameters. These parameters include power, voltage, state of charge of the battery, charging cost, grid load demand, distance from the grid to the vehicle and vehicle demand. The actor-critic algorithm serves as a guide for electric vehicle charging automation, aligning with vehicle-specific data such as charging cost, vehicle demand, and grid demand to assess switching responses and enhance result accuracy. Load variations in real and reactive power for different configurations—domestic, industrial, and vehicle connections to the grid—throughout the day are considered using the IEEE 33 standard bus system. Frequency analysis of vehicle charging, grid stability analysis, and evaluation of voltage sag and swell during faulty conditions are conducted with MATLAB and the IEEE 33 standard bus system. The results provide insights into the switching time for the charging process based on algorithmic guidelines, ensuring the process follows the correct trajectory. The study demonstrates that charging time and cost are directly proportional to demand, even in time-varying processes. In the course of this research, an embedded hardware prototype model was developed to implement the proposed concept of E-vehicle charging automation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

We report on the properties of the Power over Fiber (PoF) transmission link using a High-Power Laser Source operating at 976 nm and using three types of optical fiber with a core diameter of 50 µm. Two step-index profile multimode optical fibers and one fiber with a gradient index were used for optical power transmission. Optical light was converted to electricity using commercially available Photovoltaic Power Convertors (PPCs) with a maximal optical input power of 1.5 W and experimental PPCs with a maximal optical input power of 4.0 W. We experimentally proved optical power transmission up to a distance of 300 m. In the case of the commercially available working PPC and using the gradient index fiber we achieved a result of 0.534 W of electrical power and using the experimental PPC we achieved 0.645 W. In the case of the step-index optical fiber, the result was 1.3 W. [ABSTRACT FROM AUTHOR]

The digital economy has played a vital role in changing economic and social development patterns and has had a multifaceted impact on carbon neutrality. This paper strives to contribute to the existing research by analyzing the relationship between the digital economy and electricity consumption intensity and providing a comprehensive understanding of the mechanisms that influence the digital economy and energy consumption, particularly focusing on electricity consumption intensity through the construction of a theoretical model. To fulfill this objective, this study introduces the development level of the digital economy into the production function of representative power enterprise, constructs a partial theoretical equilibrium analysis framework, calculates provincial digital economy development indices for Chinese provinces from 2013–2021 using the CRITIC method, and examines the impact of the digital economy on power consumption intensity. The results show that the digital economy can reduce the intensity of power consumption. A unit standard deviation increase in the digital economy development level reduces power consumption intensity by 0.1008 standard deviations. The study also conducts a mechanism analysis with economic growth as the mediating variable, which shows that the digital economy promotes economic growth and reduces electricity consumption intensity. The moderating effect of the digital economy on power consumption is further examined using local government fiscal pressure as the moderating variable. The results show that the impact of the digital economy on reducing power consumption intensity decreases with the increase of government fiscal pressure. This study not only contributes to practical applications but also enhances our comprehension of the interplay between the digital economy and power consumption. On the one hand, the development of the digital economy can reduce power consumption, thus reducing carbon emissions and achieving low-carbon growth. On the other hand, central governments can promote the development of the digital economy by reducing the fiscal pressure on local governments through transfer payments. [ABSTRACT FROM AUTHOR]

The health sector is one of the components of development, social welfare and economic growth. The purpose of this study was to develop an economic evaluation model of the environmental and health costs of occupational diseases by hybrid approach. To achieve the study goal, a taxonomy of economic evaluation model of the environmental and health costs of occupational diseases has been developed. The Delphi method was used to identify health and environmental criteria and the analytic network process (ANP) method was used to weigh the sub-criteria. Finally, health and environmental cost were estimated based on the available information. Naft Subspecialty Hospital in Tehran, Iran (NSHT), was selected as the place of case study. In this study, eight and eleven sub-criteria were identified in the health and environmental sector, respectively. The ANP results indicated that the medicine and medical equipment cost criteria with a weight of 0.312 in the Medical sector, and the special and infectious waste cost criteria with a weight of 0.085 in the environmental sector were the most significant cost criteria in NSHT. Furthermore, the parametric model findings indicated that 99.84 and 0.16% of the total costs are associated with health and environmental costs, respectively. The findings indicated that 61.3% of the costs of the health sector were associated with the two sectors of medicine and medical equipment and the cost-of-service personnel, and 91.7% of the costs of the environmental sector are associated with wastewater treatment and the cost of electricity consumption. This study tried to present a quantitative model of the health and environmental costs of NSHT. Implemention of this integrated model can be a practical and effective step in allocating resources and prioritize interventions. [ABSTRACT FROM AUTHOR]

Low-carbon development of ordinary Portland cement industry is of great significance to China's target "to peak carbon dioxide emissions before 2030 and to achieve carbon neutrality before 2060". Neglecting the regional heterogeneity in China, few studies emphasized the status and developments of energy intensity and CO2 emissions of ordinary Portland cement industry in Guizhou of Southwest China. To bridge this knowledge gap, we present an annual intensity dataset of energy and CO2 emissions at plant-level for Guizhou's ordinary Portland cement industry, which involves the details of clinker rotary kilns, yearly production of clinker and cement products, fuel consumption and electricity consumption, total CO2 emission of cement products, energy intensity indicators of clinker and cement products, utilization ratio of solid-waste in clinker and cement production, and CO2 emission factors of cement products. It is an important supplement and to existing energy intensity and CO2 emissions estimates at plant-level and provincial official emissions inventories that converges all regions of China. [ABSTRACT FROM AUTHOR]

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11–2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect. [ABSTRACT FROM AUTHOR]

Globally, the carbon footprint (CF) is constantly increasing, contrasting with the decreasing trend observed for decades in the European Union (EU) countries, where EU guidelines are responsibly followed and outlined in its strategic documents. Information and communication technology (ICT) carbon emissions have historically increased in parallel with global emissions, contributing to continuous increases in ICT's CF over time, even when excluding full life cycle emissions. This study examines the impact of ICT on household electricity consumption, aiming to quantify the potential reduction of greenhouse gas (GHG) emissions through improved household energy efficiency. The methodology includes the data collection on ICT device usage in households within the city of Novi Sad (Republic of Serbia), employing the survey method that queries respondents on device quantities and their usage patterns. This study provides results for decision-makers to recognize concrete benefits from the transition to a circular economy (CE) and low-carbon emissions, which are reflected as benefits for the local community and socio-economic environment. [ABSTRACT FROM AUTHOR]

Purpose: Most life cycle inventory (LCI) datasets include location-based electricity mixes. Using these LCI datasets in combination with market-based electricity accounting in life cycle assessments (LCAs) leads to double counting of electricity from specific sources, such as renewable energy. The goal of this paper is to analyze market-based accounting by replacing location-based electricity consumption with market-based residual mixes in all processes of the ecoinvent database. The python script for this replacement procedure is openly available. Materials and methods: We use the Brightway 2 software package to replace European location-based electricity consumption in all 21,238 processes of the ecoinvent database with residual electricity mixes on the corresponding voltage level. The ecoinvent database provides residual electricity mixes for all countries involved in European trade with market-based energy attribute certificates—Guarantees of Origin. We analyze the induced changes to the database both on an individual process level and on a database-wide level. The analysis focusses on the impact category climate change but is further extended to the impact categories terrestrial acidification, freshwater eutrophication, marine eutrophication, ozone depletion, and particulate matter formation. Results and discussion: For European processes, the implementation of the residual electricity mixes leads to average changes in the life cycle impact assessment (LCIA) results for climate change of 1%, 10%, 20%, and 50% for 2958, 733, 387, and 107 processes, respectively. Changes are especially pronounced for Norwegian and Icelandic processes due to the large differences among their location-based and residual electricity mixes. For the other analyzed impact categories, the changes in LCIA results are particularly high for freshwater eutrophication. With the push towards more primary data in LCA and GHG accounting, the impacts of using residual electricity mixes instead of location-based electricity mixes need further research on basis of quantified figures, which we present in this paper. Conclusion: This paper presents an approach towards enabling more consistent market-based accounting throughout LCAs and thereby contributes to the avoidance of double counting. Since only European residual mixes are considered, the database of this paper leads to distortion of LCA results. Further, research is needed to address double counting beyond the European electricity sector. This includes an expansion of this research to a global level and other industrial sectors. Furthermore, research is needed regarding industry-specific electricity sources in LCI datasets, which may also lead to double counting. [ABSTRACT FROM AUTHOR]

The great advantage of plasma technology in harnessing abundant clean energy for electrifying and decentralizing the chemical industry holds the promise of attaining carbon neutrality. Therefore, recent research efforts have been dedicated to reducing the energy costs of plasma processes to facilitate the commercialization of this technology. However, it has been noted an inconsistency in reporting energy costs across the literature resulted from inaccurate estimation of power consumption within the system, leading to the misevaluation of the process, its underlying mechanism, and the significance of critical factors. This study comprehensively addresses these challenges by discussing and refining methods for estimating power consumption in a plasma system. Insights are drawn from our ongoing research in plasma NOx synthesis, specifically a thorough analysis of the discharge dynamics in a recently developed reactor "high-frequency spark discharge" using a high-speed camera, ICCD camera, and high-performance oscilloscope at various pulse widths of the applied voltage. The investigation revealed the importance of accounting for the post-spark period in the voltage cycle during power estimation, as it demonstrates an influence on NOx synthesis. Furthermore, the study highlighted and addressed critical errors in power measurement and energy cost estimation in the literature. It is found that a significant error, exceeding ± 70%, arises from overlooking signals delay in the setup and improper adjustment of oscilloscope functions, particularly channel impedance, data averaging, bandwidth, and sampling rate. This paper serves as a valuable guide towards establishing standardized measurements toward the precise estimation of energy costs in plasma processes. [ABSTRACT FROM AUTHOR]

Heat pumps (HPs) have emerged as a key technology for reducing energy use and greenhouse gas emissions. This study evaluates the potential switch to air-to-air HPs (AAHPs) in Toulouse, France, where conventional space heating is split between electric and gas sources. In this context, we find that AAHPs reduce heating energy consumption by 57% to 76%, with electric heating energy consumption decreasing by 6% to 47%, resulting in virtually no local heating-related CO2 emissions. We observe a slight reduction in near-surface air temperature of up to 0.5 °C during cold spells, attributable to a reduction in sensible heat flux, which is unlikely to compromise AAHPs operational efficiency. While Toulouse's heating energy mix facilitates large energy savings, electric energy consumption may increase in cities where gas or other fossil fuel sources prevail. Furthermore, as AAHPs efficiency varies with internal and external conditions, their impact on the electrical grid is more complex than conventional heating systems. The results underscore the importance of matching heating system transitions with sustainable electricity generation to maximize environmental benefits. The study highlights the intricate balance between technological advancements in heating and their broader environmental and policy implications, offering key insights for urban energy policy and sustainability efforts. Switching to air-to-air heat pumps in Toulouse cuts energy use and eliminates CO2 emissions. While efficiency is high, cities transitioning from fossil fuel heating may see increased electricity use, stressing the need for sustainable power sources. [ABSTRACT FROM AUTHOR]

As a flexible electricity pricing mechanism, critical peak pricing (CPP) is one of the important means of demand response under the electricity market. The existing CPP research does not take into account the carbon emission problem of units and weakens the difference between the use of terminal loads on critical peak days and non-critical peak days in the establishment of electricity price model, so this paper studies the feedback mechanism of CPP on coal consumption of power generation side units and proposes a dynamic CPP mechanism that takes into account terminal consumption satisfaction and coal consumption of power generation side units. Firstly, the influence mechanism of CPP on the power generation side is studied. Secondly, the consumer psychology theory is used to construct a user demand response model under critical peak days and non-critical peak days. Then, based on the difference in load usage of end users on critical peak days and non-critical peak days, a multi-objective CPP optimization model that considers the benefits of coal consumption and end user electricity expenditure on the power generation side is constructed. Finally, three scenarios were established to analyze the sensitivity of the user demand response model parameters, terminal satisfaction constraints, prices, and rate restrictions on CPP pricing, load improvement, and unit coal consumption reduction and verify the model's effectiveness. The results show that the proposed CPP optimization model has a significant effect on load improvement and carbon emission reduction; the user response gradient, terminal satisfaction, prices, and rate restrictions have a greater influence on the model optimization results, while the threshold and saturation values have little influence on the model optimization results. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition. [ABSTRACT FROM AUTHOR]

The consumption of renewable energy should increase by 300% by 2050 compared to 2010 due to the rising demand for green electricity, stringent government mandates on low-carbon fuels, and competitive biofuel production costs, thus calling for advanced methods of energy production. Here we review the use of activated carbon, a highly porous graphitic form of carbon, as catalyst and electrode for for energy production and storage. The article focuses on synthesis of activated carbon, hydrogen production and storage, biodiesel production, energy recovery, and the use of machine learning. The textural properties and surface chemistry of activated carbon can be engineered using acid and base treatments, hetero-atom doping, and optimization of the activation conditions to improve the efficiency of renewable energy production and storage. Machine learning allows to optimize the synthesis of catalysts, electrodes and bioproducts, with benefits to the biorefinery industries. [ABSTRACT FROM AUTHOR]

The development of accurate bioenergetic models, that simulate scenarios for decision making and policy planning contributions, has become a necessity in the actual climate crisis context. The objective of this work is to develop a multicriteria methodology to evaluate scenarios for assessing a region's bioenergetic potential and determining optimal location of biopower plants. This method utilizes a geographic information system that incorporates local biomass distribution, crop harvest statistics, detailed yield data, preference and exclusion maps. The approach examines the current local use of residues in the context of long-term soil preservation, employing sustainable residue removal. When it is applied to the Province of Córdoba, Argentina, specifically assessing main agricultural harvest residues, the model identifies 1.8 million tn/year of theoretical residues, primarily from corn and soybean, revalorizing only 4.1% of the available harvest agricultural residues. When employing the most commonly used approaches in the literature, which involves average crop yield values for extensive regions, our results show an underestimation of theoretical biomass up to 26%. This makes it essential to incorporate greater detail in the modeling. After optimizing five biopower plants, each with a collection radius of 60 km, it becomes possible to convert 59% of these residues into bioenergy, generating 2.3 GWh/year, covering up to 23.3% of Córdoba's annual electricity demand. The novelty of this research resides in the development of a high detailed multicriteria methodology, which facilitates the fast simulation of scenarios to identify optimal biopower plant locations, applicability to different biomass types and bioenergy forms for extensive regions. [ABSTRACT FROM AUTHOR]

As the share of renewable energy sources, which are weather dependent and consequently volatile, continues to grow, it becomes increasingly important to explore strategies for organising both electricity production and consumption to ensure system stability. People's flexibility in their energy consumption is one option to regulate the system. To better understand people's willingness to align their electricity-consuming activities with a flexible pricing system, an online survey with 962 respondents was conducted. The analysis focused on the factors influencing their willingness to shift electricity-consuming activities away from peak hours, as well as the maximum shift duration of using certain devices. The results indicate that people with more flexible lifestyles and those who perceive shifting activities as taking less effort are more willing to shift their activities and indicate longer shift durations. The data also show that attitudes towards the environment, as well as financial, ecological, and motivational factors, play a role in explaining the variance in the willingness to shift and the shift duration. To conclude, increasing flexibility in everyday life could make a valuable contribution to the optimal use of electricity resources. [ABSTRACT FROM AUTHOR]

When compared to traditional field production, controlled environment agriculture (CEA) such as greenhouses and indoor vertical farms (VF) have sustainability benefits such as reduced land use, less product transportation to consumers, improved resource and land-use efficiencies, food safety, and local food availability. Despite its potential as an environmentally beneficial complement to conventional farming, CEA has numerous issues that limits its adoption and viability as a sustainable option. This review summarizes the literature on key areas of sustainability in CEA, such as (1) sustainability challenges, (2) technologies identified to address sustainability in CEA, (3) quantification and reporting of sustainability in CEA, and (4) gaps and opportunities in addressing CEA sustainability. To filter the available literature from the databases including Web of Science, this scoping review employed a combination of the keywords "sustainability," "controlled environment agriculture," "urban farm," "vertical farm," and "indoor farm." According to the review, main obstacles in CEA were high electricity use, geographical location-related tradeoffs, and an unfavorable public perception of CEA in comparison to field production. These issues are now being addressed by optimized lighting and sensor technology, models, decision support tools to reduce electricity use, and marketing tactics to educate people about the benefits of CEA. This scoping review offers two critical areas to focus sustainability improvement efforts: lowering electrical demand and using circular techniques for organic waste and wastewater reuse in CEA to increase water, nutrient, and energy use efficiency and recovery. In addition, it discusses the techniques and approaches to sustainability assessment in CEA, particularly within the research and application contexts. This scoping review, thus, outlines strategies for enhancing sustainability in CEA, highlighting the importance of integrating circular economy principles and advanced technologies to optimize resource use, and advocates for ongoing research and education to shift public perceptions toward the sustainable potential of CEA. [ABSTRACT FROM AUTHOR]

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types. [ABSTRACT FROM AUTHOR]

The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa. Highlights: Achieving low-carbon development in Africa is possible. This research investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions in Africa. This study utilized the Panel autoregressive distributed lag model and panel data from 31 African countries. The results shed new light on the emission reduction potential of renewable energy consumption and import of goods and services. The study also shows that ICTs increase carbon emissions in Africa. This article offers recommendations concerning carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Providing sustainable, affordable, and reliable electricity through low-carbon energy development in the Nigerian energy sector is fundamental to ensuring energy security. Currently, efforts to harness the potential of renewable energy, to provide universal electricity access for all have not translated into significant economic development in Nigeria. Investment in green hydrogen could strengthen Nigeria's net-zero transition plan (NETP) and achieve sustainable energy access. The study explored the role of green hydrogen among five Electricity Distribution Companies (DisCos), from three geopolitical zones in Nigeria—North West, North Central, and North East. A bottom-up optimization linear programming methodology based on an open energy modelling framework (OEMOF) was used as the modelling paradigm. Secondary data mined from the Nigeria Energy Commission, Nigeria Electricity Regulatory Commission, NECAL 2050 report and international reports, and 2020 was used as a reference year to benchmark the model. The basic characteristics of the generation of electricity from green hydrogen, fuel cells, electrolyzers, and hydrogen storage, among other existing generation plants, were modelled till 2060 using modelled daily data obtained from Toktarova et al. (Electrical Power and Energy Systems 111:160–181, 2019). Outcomes from benchmarking led to two planning scenarios; these investigated possible insights that explored green hydrogen in Nigeria. Results showed that an integrated distributed approach would enhance harnessing green hydrogen in Nigeria, that is, electricity distribution among the DisCos. The study also revealed the following (1) the levelized cost of electricity could drop by about 8%, so also the cost of the investment; (2) access to electricity showed an improvement compared to the base year; and (3) emissions were cut in the power sector. To attain sustainable NETP with green hydrogen, the study recommends that a distributed generation approach among DisCos would support the national net-zero transition plan. [ABSTRACT FROM AUTHOR]

In Africa, mitigating climate change in a context of a growing human population and developing economies requires a bold transition to renewable energy (RE) resources. Declining costs for solar photovoltaics (by 90% between 2009 and 2023) and wind turbines (by 57% between 2010 and 2023) fuelled their construction, and hybrid forms such as floating photovoltaics (FPV) on existing hydropower reservoirs are increasingly being explored. Nevertheless, 65% of the proposed RE capacity in Africa remains hydropower, despite confirmed ecological, socioeconomic, and political ramifications on different spatiotemporal scales. The 673 proposed hydropower plants (HPPs) would increasingly affect river systems and threaten their biodiversity. While there is clear evidence that a transition to RE in Africa is technically feasible, there is a lack of spatially explicit studies on how this transition could be implemented. Hence, the aim of the present study is to explore options for an RE mix that avoids additional hydropower construction and, therefore, further river fragmentation. Attribute data of the open-accessible Renewable Power Plant Database (RePP Africa) were analysed to assess the amount of lost capacity due to operation stops. Geospatial analyses of solar irradiation and existing reservoir data were used to derive the potential for FPV. The degree of possible replacement of future hydropower was assessed under consideration of economically feasible wind and solar photovoltaic (PV) potential. To enhance electricity generation from existing HPPs, efficient and sustainable renewable power plant planning must integrate the risk of failure, as it has diminished the available capacity in the past up to 24%. Our findings further reveal that 25 African countries could replace the proposed hydropower development by FPV covering less than 25% of the surface area of their existing hydropower reservoirs. All 36 African countries could replace proposed hydroelectricity generation by fully exploiting feasible onshore wind and solar PV potential with a mean surplus of 371 TWh per year. In summary, our findings provide scientific evidence to support policy discussions on the potential electricity gains from (1) minimizing plant failure, (2) installing FPV as a co-use option, and (3) exploiting wind and solar resources. This study provides quantitative, data-based, and spatially explicit scenarios on the implementation of an RE mix that could relieve the dam building pressure on African rivers. [ABSTRACT FROM AUTHOR]

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Off-grid photovoltaic systems have been proposed as a panacea for economies with poor electricity access, offering a lower-cost "leapfrog" over grid infrastructure used in higher-income economies. Previous research examining pathways to electricity access may understate the role of off-grid photovoltaics as it has not considered reliability and carbon pricing impacts. We perform high-resolution geospatial analysis on universal household electricity access in Sub-Saharan Africa that includes these aspects via least-cost pathways at different electricity demand levels. Under our "Tier 3" demand reference scenario, 24% of our study's 470 million people obtaining electricity access by 2030 do so via off-grid photovoltaics. Including a unit cost for unmet demand of 0.50 US dollars ($)/kWh, to penalise poor system reliability increases this share to 41%. Applying a carbon price (around $80/tonne CO2-eq) increases it to 38%. Our results indicate considerable diversity in the level of policy intervention needed between countries and suggest several regions where lower levels of policy intervention may be effective. This study investigates the role of off-grid solar in achieving SDG7 in Africa, focusing on understanding the impact of carbon pricing and supply reliability. It uses high-resolution spatial analysis and demand modelling to explore policy interventions for universal electricity access. [ABSTRACT FROM AUTHOR]

As stated in the 2016 Paris Agreement, concerns about global climate change and carbon emissions have increased, and organizations, in particular, have embarked on an annual measurement process to estimate their contribution to global climate change. Carbon footprint, one of the measurement methods, is a widely applied tool to assess the environmental impact of organizations. This study presents a real case study of a denim-washing company's activities based on ISO standard calculation methods of greenhouse gas emissions. Accordingly, the annual carbon footprint of the denim-washing company was 2482.09 tCO2e for the year 2021 in total for the overall carbon footprint. Direct emission was calculated at 1575.75 tCO2e, indirect energy–related emission at 798.09 tCO2e, and indirect non-energy–related emission at 108.25 tCO2e. The highest CO2 emissions are related to heating from greenhouse gas direct emission sources, followed by purchased electricity consumption, and the lowest CO2 emissions are related to fire–CO2 tube storage. In conclusion, this study is particular in that it analyzes not only the specific processes of a denim-washing company but also the overall organizational carbon footprint calculation, assesses the importance of indirect non-energy in the total carbon footprint, and evaluates the calculation findings with sector-specific mitigation strategies. [ABSTRACT FROM AUTHOR]

Global electricity demand is increasing with the rising population and rapid urbanization. Building Integrated photovoltaic (BIPV) system is a new method of renewable energy generation where solar photovoltaic (PV) modules are integrated into the building surfaces such as façade, shades, windows, roofs, and tiles. BIPV systems reduce the urban energy demand. Utilization of vertical surfaces makes the BIPV system a preferable choice where land scarcity affects the implementation of large PV systems. The economic viability depends on the maximum power generated by the BIPV array. In urban environments, the BIPV arrays experience severe partial shading conditions (PSCs). The PSCs cause mismatch losses, reducing the global maximum power of the BIPV array and efficiency. Fixed array configurations such as series-parallel (SP), total-cross-tied (TCT), triple-tied (TT), and quarter-tied (QT) are designed to solve this issue. The cross ties across the rows of the BIPV array improve the performance at the expense of more wiring. Researchers proposed various optimal array configurations with different shading patterns. This research attempts to generalize the design of the BIPV array configurations by considering the trade-off between wiring requirements and shading losses. The performance of SP, TT, QT, and TCT configurations under four different shading conditions is simulated with the proposed BIPV array design algorithm. A 9 × 8 BIPV array of 3.6 kW is considered. QT configuration reduces the wiring requirement by 10.45% compared to TCT and improves up to 8.43% maximum power than SP. The fill factor is improved to 48.49%, and the mismatch loss is limited to 34.10%. Therefore, QT and TCT are considered favorable configurations for BIPV array design. [ABSTRACT FROM AUTHOR]

In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses decision trees constructed with multiple projections as the "base-learners", which we call "functional multi-index trees". We establish identifiability conditions for these trees and introduce two algorithms to compute them. We use numerical experiments to investigate the performance of our method and compare it with several linear and nonlinear regression estimators, including recently proposed nonparametric and semiparametric functional additive estimators. Simulation studies show that the proposed method is consistently among the top performers, whereas the performance of existing alternatives can vary substantially across different settings. In a real example, we apply our method to predict electricity demand using price curves and show that our estimator provides better predictions compared to its competitors, especially when one adjusts for seasonality. [ABSTRACT FROM AUTHOR]

Purpose: The provision of healthcare is a substantial global contributor to greenhouse gas (GHG) emissions. Several medical specialties and national health systems have begun evaluating their carbon emission contributions. The aim of this review is to summarise and describe the carbon footprint resulting from the provision of adult, paediatric and neonatal critical care. Methods: A systematic search of Embase, Cochrane and Web of Science was performed in January 2023. Studies reporting any assessment of the carbon footprint of critical care were included. No language restrictions were applied. GHG emissions from life cycle assessments (LCA) were reported, in addition to waste, electricity and water use. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed. Results: In total, 13 studies assessing and describing the environmental impact of 36 adult or paediatric intensive care units (ICUs) were included. Two studies described full LCAs, seven reported waste only, two provided audits of unused medical supplies, one reported electricity use, and one study described a Material Flow Analysis. The estimated carbon emissions from critical care range between 88 kg CO2e/patient/day and 178 kg CO2e/patient/day. The two predominant sources of carbon emissions in critical care originate from electricity and gas use, as well as consumables. Waste production ranged from 1.1 to 13.7 kg/patient/day in the 6 studies where mean waste could be calculated. Conclusion: There is a significant carbon footprint that results from intensive care provision. Consumables and waste constitute important, measurable, and modifiable components of anthropogenic emissions. There remains uncertainty due to a lack of literature, several unstudied areas of carbon emissions from critical care units, and within measured areas, measurement and reporting of carbon emissions are inconsistent. [ABSTRACT FROM AUTHOR]

With the advancement of smart power plants, electricity consumers are transitioning from a passive electricity consumption model to an intelligent consumption model. However, distributed power equipment and the vast amounts of electricity consumption data they generate pose significant information security risks. Traditional access control mechanisms cannot meet the security needs of distributed smart power plants. To address this issue, this paper introduces a distributed smart power plant access control mechanism based on blockchain and ciphertext updatable functional encryption. Firstly, the decentralized and tamper-proof features of blockchain technology ensure the integrity, transparency, and security of data transmission, effectively preventing data tampering and leakage. Secondly, by incorporating ciphertext updatable functional encryption, we guarantee data confidentiality and achieve fine-grained access control over electricity consumption data. This allows users to independently and precisely control data sharing and access. Furthermore, by deploying smart contracts with trust mechanisms on the blockchain, automated management of storage and access is achieved. Through a comprehensive evaluation of trust levels, trust values, and honesty, the system not only enhances its efficiency but also ensures that only legitimate entities can access data resources, thus preserving the utility of electricity consumption data. In addition, the adoption of decentralized unique identifiers on the blockchain for user authentication and authorization strengthens user privacy protection and data sovereignty. Lastly, experimental results demonstrate that this mechanism, while ensuring security, maintains a high level of efficiency, meeting the complex security requirements of distributed smart power plants. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) users who aim to become flexibility providers face a tradeoff between staying in control of charging and minimizing their electricity costs. The common practice is to charge immediately after plugging in and use more electricity than necessary. Changing this can increase the EV's flexibility potential and reduce electricity costs. Our extended electricity cost optimization model systematically examines how different changes to this practice influence electricity costs. Based on the Prospect Theory and substantiated by empirical data, it captures EV users' tradeoff between relinquishing control and reducing charging costs. Lowering the need to control charging results in disproportionally large savings in electricity costs. This finding incentivizes EV-users to relinquish even more control of charging. We analyzed changes to two charging settings that express the need for control. We found that changing only one setting offsets the other and reduces its positive effect on cost savings. Behavioral aspects, such as rebound effects and inertia that are widely documented in the literature, support this finding and underline the fit of our model extension to capture different charging behaviors. Our findings suggest that service providers should convince EV-users to relinquish control of both settings. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the COVID-19 pandemic on CDS, stock returns, and economic activity in the US and the five European countries that have been most affected: the UK, Germany, France, Italy, and Spain. The sample period covers the period from 11 March 2020 to 19 February 2021. In the empirical analysis, first, we estimate benchmark linear VAR models and then, given the evidence of parameter instability, TVP-VAR models with stochastic volatility, which are ideally suited to capturing the changing dynamics in both financial markets and the real economy. The linear VAR responses of CDS to the number of COVID-19 cases are positive and statistically significant, whilst those of electricity consumption are insignificant and those of stock returns vary across countries in terms of their sign and significance. The results from the TVP-VAR analysis indicate that the effects of shocks on the system variables was more pronounced during the initial stages of the pandemic and then decreased in the following months. Specifically, there was a positive impact of the number of COVID-19 cases on CDS and a negative one on stock returns and economic activity, the latter two being interlinked. [ABSTRACT FROM AUTHOR]