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Background and aims: As a part of the framework of the EU-funded Energy efficiency through Behavior CHANge Transition (ENCHANT) project, the present paper intends to provide a "Research Protocol" of a web-based trial to: (i) assess the effectiveness of behavioral intervention strategies––either single or in combination––on electricity saving, and (ii) unravel the psychological factors contributing to intervention effectiveness in households across Europe. Methods and materials: Six distinct interventions (i.e., information provision, collective vs. individual message framing, social norms, consumption feedback, competitive elements, and commitment strategies) targeting electricity saving in households from six European countries (i.e., Austria, Germany, Italy, Norway, Romania, and Türkiye) are evaluated, with an initial expected samples of about 1500 households per country randomly assigned to 12 intervention groups and two control groups, and data is collected through an ad-hoc online platform. The primary outcome is the weekly electricity consumption normalized to the last seven days before measurement per person per household. Secondary outcomes are the peak consumption during the last day before measurement and the self-reported implementation of electricity saving behaviors (e.g., deicing the refrigerator). The underlying psychological factors expected to mediate and/or moderate the intervention effects on these outcomes are intentions to save electricity, perceived difficulty of saving energy, attitudes to electricity saving, electricity saving habit strength, social norms to save electricity, personal norms, collective efficacy, emotional reaction to electricity consumption, and national identity. The intervention effectiveness will be evaluated by comparing psychological factors and consumption variables before and after the intervention, leading to a 14 (groups including 2 control groups) × 6 (time) mixed factorial design, with one factor between (group) and one factor within subjects (time)–6 measurements of the psychological factors and 6 readings of the electricity meters, which gives then 5 weeks of electricity consumption. Results: Data collection for the present RCT started in January 2023, and by October 2023 data collection will conclude. Discussion: Upon establishing feasibility and effectiveness, the outcomes of this study will assist policymakers, municipalities, NGOs, and other communal entities in identifying impactful interventions tailored to their unique circumstances and available resources. Researchers will benefit from a flexible, structured tool that allows the design, implementation and monitoring of complex interventions protocols. Crucially, the intervention participants will benefit from electricity saving strategies, fostering immediate effectiveness of the interventions in real-life contexts. Trial registration: This trial was preregistered in the Open Science Framework: https://osf.io/9vtn4. [ABSTRACT FROM AUTHOR]

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy. [ABSTRACT FROM AUTHOR]

As the share of air conditioning electricity consumption within total grid electricity consumption grows, the decomposition of such consumption becomes increasingly crucial for assessing electricity usage patterns, devising consumption scheduling strategies, and maintaining the stability of the power grid. Although there is a strong correlation between apparent temperature and air conditioning electricity consumption, the literature currently available seldom explores the impact of apparent temperature on this consumption. Moreover, there is a scarcity of effective assessment indices to evaluate the efficacy of air conditioning electricity consumption breakdown. This study introduces a method for decomposing electricity consumption from air conditioning units, utilizing effective duration as a basis to tackle these issues. By employing an apparent temperature model as a constraint, this approach identifies the effective operating time of air conditioning and constructs a constrained convex optimization problem to estimate air conditioning power usage. Additionally, a novel evaluation index for the effectiveness of air conditioning electricity consumption decomposition is proposed, which includes penalties for negative decomposed consumption, alongside the traditional consistency index. Comparative experiments are conducted using real electricity consumption data from Fujian Province. Empirical results indicate that the methodology for air conditioning electricity consumption decomposition presented in this paper aligns more closely with actual conditions. Furthermore, the evaluation metrics introduced for the decomposition of air conditioning electricity consumption are adept at precisely gauging the quality of the air conditioning electricity consumption data. [ABSTRACT FROM AUTHOR]

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method. [ABSTRACT FROM AUTHOR]

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China. [ABSTRACT FROM AUTHOR]

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development. [ABSTRACT FROM AUTHOR]

It is necessary in agricultural consulting to take into account the current problems as well as economic and social challenges facing rural areas. Undoubtedly, sustainable economy and energy policy is such a problem in Poland, including the issue of access to energy from renewable sources and financial possibilities of meeting the electricity demand of households and agriculture. Therefore, advisory and information activities in the field of reducing energy poverty and improving air quality are important. The main purpose of the paper was to identify and assess the role of advisory entities in the process of counteracting energy poverty in rural areas in Poland. The basic research method was an expert (survey) study. Research shows that the subject of energy poverty is relatively rarely taken up by consulting institutions as part of training courses, especially issues related to saving electricity in the household/agricultural sector are marginalized; monitoring and analysis of energy consumption; selection of technical devices in terms of energy efficiency. Based on expert research, there were identified consultancy activities that are and may be important in the topic under study. Experts indicated co-financing of activities in the field of energy efficiency as the preferred way to fight energy poverty in agriculture and rural areas. The results may constitute an important direction in the development of consultancy, the basis for building priority programs, which in turn may affect the behaviour and actions of farmers and other inhabitants of rural areas in the context of energy transformation. The performed research may constitute the basis for further, in-depth analyses in other countries and on an international scale. [ABSTRACT FROM AUTHOR]

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances. [ABSTRACT FROM AUTHOR]

In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings' number of floors and construction periods' dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow. [ABSTRACT FROM AUTHOR]

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA. [ABSTRACT FROM AUTHOR]

In this digital age, promoting economic development through technology innovation and adoption has become a pressing matter, contributing to increased productivity and, in turn, carbon emissions. Consequently, this study employs a novel technique (Newey–West Standard Error Method, Technology Adaptation Model) to quantify information and communication technology (ICT) adoption rates as a proxy indicator for evaluating the Persian Gulf economy's technology development. Moreover, this study investigates the evidence of the environmental Kuznets curve, with trade openness, technological adoption, and innovation as sustainable development controls. The findings reveal that two of three technological innovation instruments, fixed telephone, and internet subscriptions, increase carbon emissions. In contrast, mobile cellular subscriptions simultaneously reduce carbon emissions in the Persian Gulf. Furthermore, measures of technology adoption, high-technology exports, and electricity use contribute to the increase in carbon emissions. Trade openness also raises carbon emissions in the Persian Gulf. These findings suggest that policymakers must develop technological innovation and adoption strategies that effectively promote a greener environment. [ABSTRACT FROM AUTHOR]

Path planning for electric vehicles (EVs) can alleviate the limited cruising range and “range anxiety”. Many existing path optimization models cannot produce satisfactory solutions due to the imposition of too many assumptions and simplifications. The targeted optimal-path problem for electric vehicles (EV-TOP), which is proposed in the paper, aims at identifying the targeted optimal path for EVs under the limited battery level. It minimizes the travel cost, which is composed of the travel time and the total time that is spent at charging stations (CSs). The model is much more realistic due to the prediction and the consideration of the waiting times at CSs and more accurate approximations of the electricity consumption function and the charging function. Charging station information and the road traffic state are utilized to calculate the travel cost. The EV-TOP is decomposed into two subproblems: a constrained optimal path problem in the network (EV1-COP) and a shortest path problem in the meta-network (EV2-SP). To solve the EV1-COP, the Lagrangian relaxation algorithm, the simple efficient approximation (SEA) algorithm, and the Martins (MS) deletion algorithm are used. The EV2-SP is solved using Dijkstra’s algorithm. Thus, a polynomial-time approximation algorithm for the EV-TOP is developed. Finally, two computational studies are presented. The first study assesses the performance of the travel cost method. The second study evaluates the performance of our EV-TOP by comparing it with a well-known method. [ABSTRACT FROM AUTHOR]

The economic recovery of the tourism industry after the Covid-19 pandemic to find modern and efficient trends to increase profitability is accompanied by the adoption of comprehensive accommodation approaches towards resilience and environmental sustainability. The research aims at the application of environmental management elements and measures in all types of accommodation facilities in the Czech Republic (n1 = 1,016). A qualitative focus group method complemented the quantitative research using correspondence analysis, Levene's, Kruskal-Wallis, and Tukey's HSD tests (n2 = 9 + moderator). The results indicate that the differences in the number of environmental measures implemented were minimal for the monitored hotels and guesthouses. On the other hand, the star rating of accommodation facilities is not a key parameter in the environmental impact assessment. The most used environmental measures were devices reducing electricity consumption (hotels 94%, guesthouses 94%), separating waste (hotels 88%, guesthouses 89%), and water consumption reduction (hotels 85%, guesthouses 86%). At the same time, the most minor used were measures reducing chemical consumption (hotels 23%, guesthouses 22%) communication and environmental education of employees and guests (hotels 32%, guesthouses 18%). [ABSTRACT FROM AUTHOR]

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken. [ABSTRACT FROM AUTHOR]

In recent years, the issues related to carbon emissions and environment have attracted extensive attentions. Considering four scenarios (the energy conversion, energy capital savings and loans, energy exports and cement production carbon emissions), this paper adopts the energy consumption method and input-output method to analyze China's carbon emissions structure on the supply-side and demand-side of energy, and finally provides policy recommendations for China's structural emission reduction. The results show that, if the four influencing factors were not considered, the measurement of carbon emissions from the final demand was 44.91% higher than the baseline scenario, 12.36% lower than the baseline scenario from intermediate demand, and 10.23% lower than the baseline scenario from the total. For China's carbon emissions structure on the supply-side of energy, the carbon emissions from high-carbon energy, represented by raw coal, accounted for 66.805% of the total energy-related carbon emissions, while the carbon emissions from low-carbon energy, represented by natural gas, only accounted for 2.485%. For China's carbon emissions structure on the demand-side of energy, the carbon emissions from intermediate demand (enterprise production) accounted for more than 95% of total energy-related carbon emissions, while the carbon emissions from final demand (residents and government use) accounted for less than 5%. For each specific industry in intermediate demand for energy, the heavy industry, electric power, fossil energy, and chemical industry have high carbon emissions and low carbon emissions efficiency. However, the agriculture, construction, light industry, and service are the opposite. Finally, we provide policy recommendations for improving the accuracy of carbon emissions measurement and carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

As there is a high number of urban communities and residential activities like electricity uses, transportation, food consumption, garbage production, and knowledge on climate change and carbon emissions, these affect the resulting carbon emissions in the city. However, the studies learn about the association between household and human activities, socio-demographic characteristic, carbon emissions and climate change knowledge on urban communities carbon emissions at Jakarta are still rare. This study aimed to assess whether daily human behavior, demographic variables, climate change knowledge, and carbon emissions knowledge affecting carbon emissions of urban communities. A cross-sectional study design was performed via a structured questionnaire survey and a jejakkarbonku application towards 200 respondents who were living in the two selected urban villages of Jakarta, Indonesia (Jatinegara and East Cengkareng). The results revealed that the mean urban communities' carbon emissions in the region were 4.4 tonnes CO2 eq/th. Electricity consumption in the kitchen and dining room produced the most carbon emissions (38.6%), and waste disposal activity produced the least (0.68%). Urban carbon emissions affected by employment status and income level, but not according to food consumption, garbage production, climate change and carbon emission knowledge. Climate change and carbon emissions knowledge influenced by marital and employment status. Although not significant, in theory, these two types of knowledge contribute indirectly to carbon emissions. Further research on the low carbon diet, intervention studies to increase deep awareness on the carbon emissions and climate change is needed to develop strategies reducing carbon emissions at urban communities. [ABSTRACT FROM AUTHOR]

The study aims to investigate the impact of social, economic and political globalization on the renewable energy-economic growth nexus in a panel of six Asian emerging economies over the period 1975–2020. The results of the CS-ARDL approach show that renewable energy consumption contributes significantly to long run economic growth. Economic and political globalization firmly hold back economic growth, while social globalization directly promotes economic growth. The nonlinear effects of political, social, and economic globalization on economic growth clearly demonstrate the validity of the inverted U-shaped relationship between political globalization, economic globalization, and economic growth, and the U-shaped relationship between social globalization and economic growth. The study also found that economic, social and political globalization moderated the impact of renewable energy on boosting economic growth. Based on the renewable energy consumption model, it is revealed that economic growth significantly promotes long run renewable energy consumption. Economic, social, and political globalization have significantly boosted long run renewable energy consumption. However, the nonlinear effect model reflects a U-shaped relationship between globalization indicators and renewable energy consumption. The interaction of political, economic, and social globalization with economic growth has also witnessed an increase in renewable energy consumption, which supports the scale effect hypothesis. The causality test concludes that there is a two-way causal relationship between renewable energy consumption and economic growth, thus supporting the feedback hypothesis. The policy implications for Asian emerging economies are discussed based on the empirical analysis of this study. [ABSTRACT FROM AUTHOR]

Our study uses a small area estimation method to estimate the average and inequality of per capita kWh consumption for small areas in Vietnam. It shows evidence of a large spatial heterogeneity in the electric power consumption between districts and provinces in Vietnam. Households in the mountains and highlands consumed remarkably less electricity than those in the delta and coastal areas. Notably, we find a U-shaped relationship between the inequality of electricity consumption and economic levels in Vietnam. In poor districts and provinces, there is very high inequality in electricity consumption. Inequality is lower in middle-income districts and provinces. [ABSTRACT FROM AUTHOR]

Renewable energy holds a remarkable role in clean energy adaptation due to the much lower carbon footprint it releases compared to other fossil fuels. It also has a positive impact by slowing down the rate of climate change. The study has examined the links between renewable and non-renewable energy use, CO2 emissions and economic growth in developed, developing, and LDCs and Economies in Transition between 1990 and 2019 in 152 countries. Granger-causality has been used as the methodology to investigate the link between the variables. The findings of the existing studies on the relationship between the consumption of renewable and non-renewable energy sources and economic growth are inconsistent, indicating that there may or may not be a relationship between the two factors. Apart from having a few empirical studies so far have examined the link between the above-mentioned variables, analysis has yet to encompass all the regions in the four sub-groups discussed above. The results indicated that no Granger-causal relationship exists between GDP and REC outside of Economies in Transition. Additionally, the GDP and CO2 of all countries have a one-way relationship. Nevertheless, research indicates that GDP and CO2 have a bi-directional link in Economies in Transition, a uni-directional relationship in developing countries, and no meaningful association in developed and LDCs. Therefore, it is essential to emphasise actions to lower CO2 emissions and develop renewable energy while also stimulating the economy. Ultimately, more nations should choose renewable energy sources to build a more sustainable future. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution. [ABSTRACT FROM AUTHOR]

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption. [ABSTRACT FROM AUTHOR]

The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas. [ABSTRACT FROM AUTHOR]

Ethiopia unveiled homegrown economic reform agenda aimed to achieve a lower-middle status by 2030 and sustain its economic growth to achieve medium-middle and higher-middle status by 2040 and 2050 respectively. In this study, we evaluated the optimal renewable energy mix for power generation and associated investment costs for the country to progressively achieve upper-middle-income countries by 2050. Two economic scenarios: business as usual and Ethiopia's homegrown reform agenda scenario were considered. The study used an Open Source energy Modeling System. The model results suggest: if projected power demand increases as anticipated in the homegrown reform agenda scenario, Ethiopia requires to expand the installed power capacity to 31.22GW, 112.45GW and 334.27GW to cover the current unmet and achieve lower, medium and higher middle-income status by 2030, 2040 and 2050 respectively. The Ethiopian energy mix continues to be dominated by hydropower and starts gradually shifting to solar and wind energy development towards 2050 as a least-cost energy supply option. The results also indicate Ethiopia needs to invest about 70 billion US$ on power plant investments for the period 2021–2030 to achieve the lower-middle-income electricity per capita consumption target by 2030 and staggering cumulative investment in the order of 750 billion US$ from 2031 to 2050 inclusive to achieve upper-middle-income electricity consumption rates by 2050. Ethiopia has enough renewable energy potential to achieve its economic target. Investment and financial sourcing remain a priority challenge. The findings could be useful in supporting decision-making concerning socio-economic development and investment pathways in the country. [ABSTRACT FROM AUTHOR]

Large cities are more productive and generate more output per person. Using data from the UK on energy demand and waste generation, we show that they are also more energy-efficient. Large cities are therefore greener than small towns. The amount of energy demanded and waste generated per person is decreasing in total output produced, that is, energy demand and waste generation scale sublinearly with output. Our research provides the first direct evidence of green urbanization by calculating the rate at which per capita electricity use and waste decrease with city population. The energy demand elasticity with respect to city output is 83%: as the total output of a city increases by one percent, energy demand increases less than one percent, and the Urban Energy Premium is therefore 17%. The energy premium by source of energy demand is from households (13%), transport (20%), and industry (16%). Similarly, we find that the elasticity of waste generation with respect to city output is 90%. For one percent increase in total city output, there is a less than one percent increase in waste, with an Urban Waste Premium of 10%. Because large cities are energy-efficient ways of generating output, energy efficiency can be improved by encouraging urbanization and thus green living. We perform a counterfactual analysis in a spatial equilibrium model that makes income taxes contingent on city population, which attracts more people to big cities. We find that this pro-urbanization counterfactual not only increases economic output but also lowers energy consumption and waste production in the aggregate. [ABSTRACT FROM AUTHOR]

Background: The usage status, waste electrical and electronic equipment (WEEE) related to the smartphones of on-campus engineering students should be studied. Furthermore, the correlations between their smartphone profiles with energy consumption and environmental knowledge should be understood make measures improve their environmental behaviors. Methods: Pro-environmental behavior and smartphone uses of the on-campus engineering undergraduates in Xi'an, China, were investigated with a self-designed questionnaire anonymously. The energy-saving activities they participated in and their e-waste treatment patterns were analyzed. Results: Most of the respondents had a smartphone with large screen and high battery capacity, which also had long standby/usage time and frequent charging. Average daily power consumption of one smartphone was estimated to be 6.475 Wh. The surveyed undergraduate students changed their smartphones frequently, which produced large quantities of WEEE annually. Conclusion: Most on-campus students treated their waste smartphones in the proper ways. However, some of them were short of environmental knowledge about their smartphones. Some measures were suggested to improve their environmental concerns. The findings will help the on-campus engineering undergraduates in China to use their smartphones rationally and to deal with their waste smartphones appropriately. [ABSTRACT FROM AUTHOR]

Among the challenges of sustainable management of meat production, the key issue is to improve the energy efficiency of production processes, which will consequently affect the reduction of greenhouse gas emissions. Such effects are achieved by combining various chilling systems with electrical stimulation that determines the quality of meat at the slaughter stage. The novelties of the research undertaken included determining the impact of various variants of meat production (chilling method: slow, fast, accelerated + HVES/NES) on changes in the basic (industrial) quality indicators (pH and temperature) of beef produced from Polish Holstein-Friesian breed cattle, and then indicating the optimal variant for energy-efficient (sustainable) beef production. The HVES and the fast chilling method yielded positive economic (meat weight loss), technological (high quality, hot-boning), energetic (lower electricity consumption), and organizational effects (reduced chilling and storage surfaces and expenditures for staff wages) compared to the slow and accelerated methods. Reaching the desired final temperature with an increased amount of chilled meat enables obtaining a few-fold decrease in the specific energy consumption and a higher energy efficiency of the process. This allows recommending the above actions to be undertaken by entrepreneurs in the pursuit of sustainable meat production. [ABSTRACT FROM AUTHOR]

Neurons rely on localized mitochondria to fulfill spatially heterogeneous metabolic demands. Mitochondrial aging occurs on timescales shorter than the neuronal lifespan, necessitating transport of fresh material from the soma. Maintaining an optimal distribution of healthy mitochondria requires an interplay between a stationary pool localized to sites of high metabolic demand and a motile pool capable of delivering new material. Interchange between these pools can occur via transient fusion / fission events or by halting and restarting entire mitochondria. Our quantitative model of neuronal mitostasis identifies key parameters that govern steady-state mitochondrial health at discrete locations. Very infrequent exchange between stationary and motile pools optimizes this system. Exchange via transient fusion allows for robust maintenance, which can be further improved by selective recycling through mitophagy. These results provide a framework for quantifying how perturbations in organelle transport and interactions affect mitochondrial homeostasis in neurons, a key aspect underlying many neurodegenerative disorders. Author summary: Neurons contain long projections termed axons and dendrites and a small central body that is responsible for much of cellular biosynthesis. Mitochondria, the energy hubs of a cell, are synthesized in the soma and actively transported to distant sites of high energy demand. Given the extreme distances between these sites and the soma, maintaining distal mitochondrial health poses a substantial challenge. Defects in mitochondrial transport and maintenance are associated with several neurological disorders. Fortunately, mitochondria stationed at distant sites can be 'serviced' by passing mitochondria that emerge from the soma and move around the neuron, as well as through low levels of local protein synthesis. We develop mathematical models for two strategies of mitochondrial maintenance: one with direct protein exchange between moving and stationary mitochondria ('Space Station') and the other with moving mitchondria occasionally replacing stationary ones at the demand sites ('Changing of the Guard'). We find that only a few servicing events and a small motile pool form optimal conditions for maintaining mitochondrial health. The system can be improved further by selectively removing and recycling some unhealthy mitochondria. Our results are consistent with observations of mitochondrial behavior in neurons and form a basis for future quantitative study of mitochondrial maintenance. [ABSTRACT FROM AUTHOR]

A comprehensive life cycle assessment (LCA) was conducted for the matchsticks industry in the Khyber Pakhtunkhwa province of Pakistan to quantify environmental footprint, water footprint, cumulative energy use, and to identify improvement opportunities in the matchsticks manufacturing process. One carton of matchsticks was used as reference unit for this study. Foreground data was collected from the matchsticks industry through questionnaire surveys, personal meetings, and field measurements. The collected data was transformed into potential environmental impacts through the Centre for Environment Studies (CML) 2000 v.2.05 method present by default in the SimaPro v.9.1 software. Water footprint was calculated using methodology developed by Hoekstra et al., 2012 (water scarcity index) V1.02 and cumulative energy demand by SimaPro v.9.1 software. The results showed that transport of primary material (wood logs), sawn wood for matchsticks, red phosphorous, acrylic varnish, and kerosene fuel oil contributed to the overall environmental impacts. Transport of primary materials and sawn timber for matchsticks contributed significantly to abiotic depletion, global warming, eutrophication potential, ozone depletion, corrosion, human toxicity, and aquatic ecotoxicity effects. The total water footprint for manufacturing one carton of matchsticks was 0.265332 m3, whereas the total cumulative energy demand was 715.860 Mega Joules (MJ), mainly sourced from non-renewable fossil fuels (708.979 MJ). Scenario analysis was also conducted for 20% and 30% reduction in the primary material distance covered by trucks and revealed that reducing direct material transport distances could diminish environmental impacts and energy consumption. Therefore, environmental footprint could be minimized through diverting matchsticks industries freight from indigenous routes to high mobility highways and by promoting industrial forestry close to industrial zones in Pakistan. Many industries did not have emissions control systems, exceeding the permissible limit for emissions established by the National Environmental Quality Standards (NEQS) of Pakistan. Thus, installation of emissions control system could also diminish emissions from match industry in Pakistan. [ABSTRACT FROM AUTHOR]

The energy demand in the world has increased rapidly in the last few decades. This demand is arising the need for alternative energy resources. Solar energy is the most eminent energy resource which is completely free from pollution and fuel. However, the problem occurs when it comes to efficiency under different atmospheric conditions such as varying temperature and solar irradiance. To achieve its maximum efficiency, an algorithm of maximum power point tracking (MPPT) is needed to fetch maximum power from the photovoltaic (PV) system. In this article, a nonlinear backstepping terminal sliding mode control (BTSMC) is proposed for maximum power extraction. The system is finite-time stable and its stability is validated through the Lyapunov function. A DC-DC buck-boost converter is used to deliver PV power to the load. For the proposed controller, reference voltages are generated by a radial basis function neural network (RBF NN). The proposed controller performance is tested using the MATLAB/Simulink tool. Furthermore, the controller performance is compared with the perturb and observe (P&O) MPPT algorithm, Proportional Integral Derivative (PID) controller and backstepping MPPT nonlinear controller. The results validate that the proposed controller offers better tracking and fast convergence in finite time under rapidly varying conditions of the environment. [ABSTRACT FROM AUTHOR]

The balancing of supplied energy to energy demand is often very challenging due to unstable power supply and demand load. This challenge causes the level of performance of distribution networks to be lower than expected. Research has however, shown the role of demand response (DR) on the performance of power networks. This work investigates the influence of DR, in the presence of incorporated renewable energy, on technical loss reduction, reliability, environment, energy saved and incentives paid to consumers with the help of PSAT and AIMMS software. Results from simulation have shown that the introduction of renewable energy into a Ghanaian distribution network coupled with implementing the proposed DR improves total energy supply by 9.8% at a corresponding operation cost reduction of 72.79%. The GHG and technical loss reduced by 27.26% and 10.09% respectively. The total energy saving is about 105kWh and 5,394.86kWh, for domestic and commercial loading profiles, respectively. Incentives received by consumers range between 45.14% and 58.55% more than that enjoyed, without renewable energy, by domestic and commercial consumers. The utility benefit also increased by 76.96% and 67.31% for domestic and commercial loads than that without renewable energy. Network reliability improves with implementation of DR. However, the reliability of a grid-connected network is better with a diesel generator only than with the integration of renewable energy. The power distribution companies, therefore, need to consider the implementation of incentive-based demand response program. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy, fracturing technology is widely used in oilfield operations over the last decades. Typically, fracturing fluids contain various additives such as cross linkers, thickeners and proppants, and so forth, which makes it possess the properties of considerably complicated components and difficult processing procedure. There are still some difficult points needing to be explored and resolved in the hydroxypropyl guar gum (HPG) removal process, e.g., high viscosity and removal of macromolecular organic compounds. Our works provided a facile and economical HPG removal technology for fracturing fluids by designing a series of processes including gel-breaking, coagulation and precipitation according to the diffusion double layer theory. After this treatment process, the fracturing fluid can meet the requirements of reinjection, and the whole process was environment friendly without secondary pollution characteristics. In this work, the fracturing fluid were characterized by scanning electron microscopy (SEM), Energy dispersive X-ray (EDX), X-ray diffraction (XRD) and Fourier transformed infrared (FTIR) spectroscopy technologies, etc. Further, the micro-stabilization and destabilization mechanisms of HPG in fracturing fluid were carefully investigated. This study maybe opens up new perspective for HPG removal technologies, exhibiting a low cost and strong applicability in both fundamental research and practical applications. [ABSTRACT FROM AUTHOR]

Background: Surgery risks increase when electricity is accessible but unreliable. During unreliable electricity events and without data on increased risk to patients, medical professionals base their decisions on anecdotal experience. Decisions should be made based on a cost-benefit analysis, but no methodology exists to quantify these risks, the associated hidden costs, nor risk charts to compare alternatives. Methods: Two methodologies were created to quantify these hidden costs. In the first methodology through research literature and/or measurements, the authors obtained and analyzed a year's worth of hour-by-hour energy failures for four energy healthcare system (EHS) types in four regions (SolarPV in Iraq, Hydroelectric in Ghana, SolarPV+Wind in Bangladesh, and Grid+Diesel in Uganda). In the second methodology, additional patient risks were calculated according to time and duration of electricity failure and medical procedure impact type. Combining these methodologies, the cost from the Value of Statistical Lives lost divided by Energy shortage ($/kWh) is calculated for EHS type and region specifically. The authors define hidden costs due to electricity failure as VSL/E ($/kWh) and compare this to traditional electricity costs (always defined in $/kWh units), including Levelized Cost of Electricity (LCOE also in $/kWh). This is quantified into a fundamentally new energy healthcare system risk chart (EHS-Risk Chart) based on severity of event (probability of deaths) and likelihood of event (probability of electricity failure). Results: VSL/E costs were found to be 10 to 10,000 times traditional electricity costs (electric utility or LCOE based). The single power source EHS types have higher risks than hybridized EHS types (especially as power loads increase over time), but all EHS types have additional risks to patients due to electricity failure (between 3 to 105 deaths per 1,000 patients). Conclusions: These electricity failure risks and hidden healthcare costs can now be calculated and charted to make medical decisions based on a risk chart instead of anecdotal experience. This risk chart connects public health and electricity failure using this adaptable, scalable, and verifiable model. [ABSTRACT FROM AUTHOR]

In this study, an enzymatic pathway has been developed to replicate the Calvin Cycle by creating the individual steps of the carbon cycle in a bioreactor. The technology known as "artificial photosynthesis" converts CO2 emissions into a variety of intermediates that serve as precursors to high-value products. CO2, light, water, and electricity were used as feedstock. An electrochemical reactor was also studied for the regeneration of active NADH operating at constant electrode potential. Initially, a batch electrochemical reactor containing 80 mL of 0.2 mM NAD+ in Tris-buffer (pH 7.40) was used to evaluate the electrode material operating at normal temperature and pressure. The results showed that the cathode is highly electrocatalytically efficient and selective to regenerate 97.45±0.8% of NADH from NAD+ at electrode potential of -2.3 V vs. mercury standard electrode (MSE). The NADH regeneration system was then integrated with ATP regeneration system and bioreactor containing Ribulose bisphosphate carboxylase/oxygenase (RuBisCO). NADH was regenerated successfully during the process electrochemically and then was used by the enzymatic reaction to produce triose phosphate and 3-Phosphoglycerate (3GPA). [ABSTRACT FROM AUTHOR]

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries. [ABSTRACT FROM AUTHOR]

Various countries have pledged to carry out system-wide energy transitions to address climate change. This requires taking strategic decisions with long-term consequences under conditions of considerable uncertainty. For this reason, many actors in the energy sector develop model-based scenarios to guide debates and decision-making about plausible future energy systems. Besides being a decision support instrument for policy-makers, energy scenarios are widely recognized as a way of shaping the expectations of experts and of influencing energy policy more generally. However, relatively little is known about how energy scenarios shape preferences and expectations of the public. We use an explorative research design to assess the publics' expectations of future energy systems through an online survey among Swiss residents (N = 797). We identified four significantly different clusters of people with distinct expectations about the future energy system, each seeing different implications for the acceptability of energy policies and the compatibility with projections of techno-economic energy scenarios. Cluster 1 expects a system-wide energy transition towards renewable energy sources that is similar to the policy-relevant national energy scenario. Cluster 2 also expects an energy transition, but believes it will lead to a range of technical challenges, societal conflicts and controversies with neighboring countries. Cluster 3 is the only cluster not expecting significant changes in the future energy system and thus not anticipating an energy transition. Cluster 4's expectations are between cluster 1 and 2, but it anticipates a huge increase in per capita electricity demand while prices are expected to remain low. The study at hand offers some initial insights into the interdependencies between energy transition pathways outlined in techno-economic energy scenarios and the energy system expectations of the public. These insights are essential for gaining a better understanding of whether and how energy scenarios can contribute to informed public debates about energy futures and how desirable pathways towards them might look like. [ABSTRACT FROM AUTHOR]

Smart energy meters generate real time, high frequency data which can foster demand management and response of consumers and firms, with potential private and social benefits. However, proper statistical techniques are needed to make sense of this large amount of data and translate them into usable recommendations. Here, we apply Functional Data Analysis (FDA), a novel branch of Statistics that analyses functions—to identify drivers of residential electricity load curves. We evaluate a real time feedback intervention which involved about 1000 Italian households for a period of three years. Results of the FDA modelling reveal, for the first time, daytime-indexed patterns of residential electricity consumption which depend on the ownership of specific clusters of electrical appliances and an overall reduction of consumption after the introduction of real time feedback, unrelated to appliance ownership characteristics. [ABSTRACT FROM AUTHOR]

After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE). However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan’s NDC (nationally determined contribution) assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price). Our result clearly shows that consumers’ electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%–6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2–2.26 MtCO2 (−4.5% on average compared to the zero-ECE case). The time-varying ECE is necessary for predicting Japan’s electricity demand and CO2 emissions after the earthquake. [ABSTRACT FROM AUTHOR]

In a smart grid, the power service provider enables the expected power generation amount to be measured according to current power consumption, thus stabilizing the power system. However, the data transmitted over smart grids are not protected, and then suffer from several types of security threats and attacks. Thus, a robust and efficient authentication protocol should be provided to strength the security of smart grid networks. As the Supervisory Control and Data Acquisition system provides the security protection between the control center and substations in most smart grid environments, we focus on how to secure the communications between the substations and smart appliances. Existing security approaches fail to address the performance-security balance. In this study, we suggest a mitigation authentication protocol based on Elliptic Curve Cryptography with privacy protection by using a tamper-resistant device at the smart appliance side to achieve a delicate balance between performance and security of smart grids. The proposed protocol provides some attractive features such as identity protection, mutual authentication and key agreement. Finally, we demonstrate the completeness of the proposed protocol using the Gong-Needham- Yahalom logic. [ABSTRACT FROM AUTHOR]

Woody feedstocks will play a critical role in meeting the demand for biomass-based energy products in the US. We developed an integrated model using comparable system boundaries and common set of assumptions to ascertain unit cost and greenhouse gas (GHG) intensity of electricity and ethanol derived from slash pine (Pinus elliottii) at the production and consumption levels by considering existing automobile technologies. We also calculated abatement cost of greenhouse gas (GHG) emissions with respect to comparable energy products derived from fossil fuels. The production cost of electricity derived using wood chips was at least cheaper by 1 ¢ MJ−1 over electricity derived from wood pellets. The production cost of ethanol without any income from cogenerated electricity was costlier by about 0.7 ¢ MJ−1 than ethanol with income from cogenerated electricity. The production cost of electricity derived from wood chips was cheaper by at least 0.7 ¢ MJ−1 than the energy equivalent cost of ethanol produced in presence of cogenerated electricity. The cost of using ethanol as a fuel in a flex-fuel vehicle was at least higher by 6 ¢ km−1 than a comparable electric vehicle. The GHG intensity of per km distance traveled in a flex-fuel vehicle was greater or lower than an electric vehicle running on electricity derived from wood chips depending on presence and absence of GHG credits related with co-generated electricity. A carbon tax of at least $7 Mg CO2e−1 and $30 Mg CO2e−1 is needed to promote wood-based electricity and ethanol production in the US, respectively. The range of abatement cost of GHG emissions is significantly dependent on the harvest age and selected baseline especially for electricity generation. [ABSTRACT FROM AUTHOR]