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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]

Featured Application: Improving the measurement of electricity consumption by reducing errors is the main objective of this article. As a concrete application, the presented algorithm is used for the determination of electricity meters by the end user. With minimal costs, a correct assessment of energy consumption can be made, and therefore the calculated electricity costs should be accurate. Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs. [ABSTRACT FROM AUTHOR]

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users' social network interactions to gain popularity. By incorporating the users' mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users' electricity charges. [ABSTRACT FROM AUTHOR]

This paper is one of the first which provides an in-depth quantitative analysis of how the development of the tourism industry impacts on the size of the shadow economy of a specific destination. The paper employs time-series techniques and annual data from 1960 to 2018 from Turkey. First, the size of the shadow economy is estimated using the electricity consumption method. The estimates are then used to assess the effect of tourism development on the size of the shadow economy. The findings show that there is a negative relationship between the two. A 1% increase in international tourism arrivals leads to a 0.21% and 0.316% fall in the size of the shadow economy in the short-run and long-run respectively. It implies that as the tourism sector develops, it becomes instrumental in modernising the Turkish economy leading to the movement of resources from the shadow economy to the formal sector. In the long-run the effect of the tourism industry in reducing the size of the shadow economy surpasses that of the financial sector of Turkey. [ABSTRACT FROM AUTHOR]

The globe is now in ecological turmoil as a result of the unrelenting increase in global warming. As a result, governments worldwide are committing to decarbonizing the environment, with the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) playing an important role in this effort. Hence, this paper evaluates the nonlinear (asymmetric) impact of natural gas consumption on renewable energy consumption and economic growth in the KSA and the UAE utilizing data stretching from 1990 to 2020. The study also considers other drivers of renewable energy consumption and economic growth, such as trade openness and CO2 emissions. The study utilizes nonlinear autoregressive distributed lag (ARDL) to evaluate these associations. The outcomes of bounds nonlinear ARDL (NARDL), affirm the long-run association between the variables in both countries. The nonlinear results show that positive and negative shocks in natural gas consumption have a negative impact on renewable energy in both UAE and KSA. In contrast, positive and negative shocks in natural gas consumption impact economic growth positively. The study proposed vital policy recommendations based on these results. [ABSTRACT FROM AUTHOR]

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. [ABSTRACT FROM AUTHOR]

The demand for different energy goods and services is a fundamental component in a country's economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional effects of elasticities and the application of them to simulate household responses to price changes, as well as a calculation of the welfare impacts on poor and rich households in Mexico, should inform policy design. This paper uses the Household Income and Expenditure Survey (ENIGH) from 1996 to 2018 to estimate the demand of Mexican households for fuels, specifically electricity, liquefied petroleum gas, and gasoline. A Quasi Ideal Quadratic Demand System (QUAIDS) is employed to analyse the effects of removing energy subsidies and introducing a carbon tax. The results indicate that welfare losses would be regressive concerning electricity price increases, while changes in gasoline prices would be progressive. Redistributing the tax revenues accrued by removing energy subsidies and imposing the carbon tax would have more progressive effects on the economy of Mexican households, with welfare gains of up to 350% for the poorest households in the case of electricity consumption taxes. [ABSTRACT FROM AUTHOR]

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China. [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]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [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]

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]

Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber. [ABSTRACT FROM AUTHOR]

Regarding the electricity decarbonization decision problem under the carbon cap-and-trade mechanism, this paper constructs a two-level electricity supply chain composed of an electricity generator and an electricity retailer. The electricity generator invests in renewable energy or low-carbon technologies to achieve decarbonization. This paper compares the equilibrium solutions of the models with only cap- and trade -mechanisms, renewable energy investment, and low-carbon technology investment. The main conclusions are: (1) Compared with renewable energy investment, the cost of investing in low-carbon technology and the decarbonization contribution rates are higher. (2) The investment in renewable energy increases with unit carbon emissions of traditional energy, but the investment in low-carbon technology decreases. (3) Low-carbon technology investment can provide the supply chain with the highest electricity demand and profits.Key words: Cap-and-trade mechanisms; Decarbonization; Electricity supply chain [ABSTRACT FROM AUTHOR]

Alternative solvents based on aqueous solutions of amino acids have been recently developed as possible substitutes for Mono Ethanol Amine (MEA) for CO2 removal from flue gas streams. The potassium taurate solvent has the advantages of degradation resistance, low toxicity and low energy requirements for its regeneration. With any type of solvent, CO2 removal applied to a power production plant decreases the revenues obtained from selling electricity because of the energy requirements. Operating the CO2 removal section in flexible mode avoids significant effects on the profits of the power plant, while accomplishing environmental regulations. This work is the first journal paper focusing on the application in flexible mode of the potassium taurate system for treating a flue gas stream from a 500 MW coal-fired power plant. Techno-economic evaluations are performed to determine the best operating conditions considering the variation in the electricity demand and its price, and different values of carbon tax. In the summer period, with high electricity prices and demands, carbon tax values between 45 EUR/tCO2 and 60 EUR/tCO2 favor CO2 absorption in the flexible mode, without periods of full CO2 emissions during the day. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

The deployment of energy storage systems can play a role in peak and frequency regulation, solve the issue of limited flexibility in cleaner power systems in China, and ensure the stability and safety of the power grid. This paper presents a comprehensive environmental impact analysis of a lithium iron phosphate (LFP) battery system for the storage and delivery of 1 kW-hour of electricity. Quantities of copper, graphite, aluminum, lithium iron phosphate, and electricity consumption are set as uncertainty and sensitivity parameters with a variation of [90%, 110%]. The results show that global warming potential is 9.08E+01 kg CO2 eq. and fossil resource use is 1.21E+03 MJ, with uncertainty ranges of [8.54E+01, 9.23E+01] and [1.15E+03, 1.23E+03], respectively. Electricity consumption during the manufacture and installation process is the greatest contributor to climate change (CO2 eq. emissions), accounting for 39.71% and largely owing to non-renewable sources, followed by cathode materials at 27.85% and anode materials at 18.36%. The disposal and recycling process offers emission reductions but requires an additional 1.17% use of fossil resources. Sobol T indices for the quantity of electricity are the highest for acidification, climate change, fossil resource use, and ionizing radiation. By considering the pathway of China's electricity mix from 2020 to 2050 under the Paris Agreement's 2° target, the potential for environmental emission reduction in the system is evaluated. The results show that the greener electricity mix could lead to a 24.59% reduction in acidification impact, a 35.74% reduction in climate change impact, a 33.24% reduction in fossil resource use, and a 44.13% reduction in ionizing radiation impact. This study offers a comprehensive view of the environmental impact reductions associated with the lithium iron phosphate battery and its industry. [ABSTRACT FROM AUTHOR]

The existence of chaos is particularly relevant, as the identification of a chaotic behavior in a time series could lead to reliable short-term forecasting. This paper evaluates the existence of nonlinearity and chaos in the underlying process of the spot prices of the Spanish electricity market. To this end, we used daily data spanning from 1 January 2013, to 31 March 2021 and we applied a comprehensive framework that encompassed a wide range of techniques. Nonlinearity was analyzed using the BDS method, while the existence of a chaotic structure was studied through Lyapunov exponents, recurrence plots, and quantitative recurrence analysis. While nonlinearity was detected in the underlying process, conclusive evidence supporting chaos was not found. In addition, the generalized autoregressive conditional heteroscedastic (GARCH) model accounts for part of the nonlinear structure that is unveiled in the electricity market. These findings hold substantial value for electricity market forecasters, traders, producers, and market regulators. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees' increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building's micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building's electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset's rating, the electrolyzer units' capacity and the tilt angle of the rooftop's photovoltaic panels, which minimize the building's interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle. The measurements were conducted during the summer season, and two models were suggested to calculate the overall efficiency of the PV/T system. The variance between the results obtained from the two models was minimal, below 5%. For the examined panel type, the following average values were derived: electrical efficiency = 12.01%, thermal efficiency = 47.21%, and overall efficiency = 59.23%. [ABSTRACT FROM AUTHOR]

Over time, several relationships have been defined between electricity consumption and a region's social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca. [ABSTRACT FROM AUTHOR]

Integrating Renewable Energy (RE) into Distribution Power Networks (DPNs) is a choice for efficient and sustainable electricity. Controlling the power factor of these sources is one of the techniques employed to manage the power loss of the grid. Capacitor banks have been employed to control phantom power, improving voltage and reducing power losses for several decades. The voltage sag and the significant power losses in the Iraqi DPN make it good evidence to be a case study proving the efficiency enhancement by adjusting the RE power factor. Therefore, this paper studies a part of the Iraqi network in a windy and sunny region, the Badra-Zurbatya-11 kV feeder, in the Wasit governorate. A substation of hybrid RE sources is connected to this feeder to compensate for the low voltage and high power losses within three different electricity demand conditions: low, medium, and high. The results are compared with a case when an optimum capacitor placement is applied. By using the CYME 9 software for analysis, the research shows the benefits of adding RE to the grid. The results have approved the hybrid RE penetration benefits as the power losses are reduced in low, medium, and high scenarios by 36.26%, 43%, and 48.4%, respectively, besides the voltage sag elimination. [ABSTRACT FROM AUTHOR]

The literature is inundated with the claim that Bitcoin pollutes the environment. While the assertion is irrefutable, the unsettled issue is whether the indictment of environmental disaster is disproportionate or distorted. This is an empirical question; this paper evaluates it via two econometric methods. First, accepting carbon dioxide (CO2) emission as a proxy for environmental pollution, the paper quantifies the elasticity of CO2 emission in relation to electricity consumption in Bitcoin production. The results reveal that, while Bitcoin production based on conventional electricity is inelastic, carbon emission responsiveness to fossil fuel is significant. A 1% increase in Bitcoin's usage of coal-generated electricity leads to a 1.64% surge in CO2 emission. Second, the study applies the error correction model to show that some electricity consumption shocks emanate from global coal prices driven by economic factors beyond Bitcoin's control. This raises the question of whether Bitcoin should shoulder the entire blame for the 1.64% pollution responsiveness. Therefore, the study makes two important contributions. First, Bitcoin's pollution impact varies according to timespan and electricity source. Second, the intensity of carbon emission from electricity consumption is aggravated by external market factors beyond Bitcoin's control. The findings should inform policymakers and enlighten environmental advocates. [ABSTRACT FROM AUTHOR]

Air-cooled heat exchangers in various industries enjoy an increasingly wider use thanks to their environmental friendliness and absence of the need for cooling water. Air-cooled heat exchangers from annular finned tubes are widely used in chemical, petrochemical, oil-refining, gas, and food industries, and also at automobile compressed natural gas filling stations (NGV gas stations), in heat power engineering, electric power engineering, refrigeration technology, and at nuclear power plants. The thermal capacity of air-cooled heat exchangers is largely determined by the characteristics of the motion of a flow of cooling air through them that can be organized with consumption of electric power by means of a ventilator or in an energy-saving regime by free convection. Unfortunately, the region of ambient air temperatures at which it is possible to use air-cooled heat exchangers under the conditions of free convection is limited. Low heat transfer intensity compared to forced convection is the main drawback of the free-convection regime which is usually compensated by an increase in a heat-emitting area, namely, by finning the heat transfer surface. By equipping air-cooled heat exchangers with additional devices making it possible to intensify free convection, the thermal performance of heat exchangers can be improved significantly. The paper provides an overview of experimental and numerical investigations of free-convection heat transfer in annular finned tube bundles with an exhaust shaft installed above them. [ABSTRACT FROM AUTHOR]

With the advancement of technology, time series data are automatically collected without human intervention. As the data collection process becomes effortless, the next change encountered is to identify the best method to forecast time series data with high accuracies. In this regard, hybrid approaches have gained much attention where the strengths of two approaches can be combined to lessen the weaknesses of each individual approach. When exploring the features of time series data, some depict repetitive patterns and also data can be observed at several levels. The repeating curves can be considered as the higher level, whereas each individual observation can be considered as the lower level. Thus, in order to handle the data in a more effective way, the series can be handled at two levels by giving prominence to the features of each level separately. This paper proposes a novel algorithm named two level neuro-functional forecaster, which is capable of handling data at two levels, hybridizing a statistical approach with an artificial intelligence approach, to gain high accuracy levels. In addition, as this approach handles data at two levels, data sparsity at a particular level can be accommodated at the other level. To apply this algorithm to a real world dataset, electricity demand data in Sri Lanka was considered where the series consisted of daily load curves with repetitive pattern across the days. The proposed hybrid algorithm, outperforms the two approaches when individually used, with a MAPE of 3.324% for a year. [ABSTRACT FROM AUTHOR]

The common grey prediction models include the GM(1, 1) model, the GM(1, 1) power model, the GM(1, N) model, the GM(N, 1) model and so on, and they have different applicable scopes. The paper proposes a method to build a novel grey Riccati model and gives a general analytic expression of time response equation for simulation and predictions and the parameter optimization method of grey Riccati model. To improve the modeling precision of the grey Riccati model and the adaptability of data to the model, the paper improves the accumulated generating sequence of original data, i.e. making a new data transformation, and the parameters in its data transformation are simultaneously optimized with the generation coefficients of the 2 background values, which greatly improves the modeling accuracy. The paper builds the grey Riccati models for China's total electricity consumption and China's private car ownership. The results show that the grey Riccati models built with the method proposed have high simulation and prediction precision. The model proposed enriches the grey model system and has great significance for the in-depth research and generalized application of grey model. [ABSTRACT FROM AUTHOR]

Purpose. Determining the impact of the route topology factor on the costs of mechanical work of an electric vehicle is the main task of this work. The impact is determined by calculating the costs of mechanical work during the movement of an electric vehicle, taking into account energy recovery. The task also includes assessment of the forces acting on an electric vehicle using the example of the 2014 Nissan Leaf AZEO. Methodology. The paper uses a mathematical model that estimates the amount of mechanical work required to overcome one of the chosen routes, taking into account energy recovery. Evaluation is performed using the most common standardized cycle WLTC class 3b. Findings. The result of the research is a developed mathematical model that will allow one to effectively estimate the amount of mechanical work to overcome the given route and the possible recovery energy. The proposed method makes it possible to determine the most economical route from the starting point to the destination, taking into account the cost of mechanical energy. Originality. A description of the main components affecting the consumption of electricity is given, taking into account the full picture of the forces acting on the electric vehicle during movement. Practical value. The obtained results are of practical importance for choosing the most optimal route of the electric vehicle, which contributes to the efficient use of energy. The proposed technique can be used in practice to plan routes from the point of view of maximum energy recovery. [ABSTRACT FROM AUTHOR]

This paper investigates the microclimate in a conference room with thermal stratification, taking as a case study the chapel of Villa San Saverio, now the seat of the "Scuola Superiore" of the University of Catania (Italy). Surveys of the former chapel were conducted to monitor air temperature and relative humidity. Subsequently, the investigation relied on numerical simulations of a simplified computational fluid dynamics (CFD) model built with the DesignBuilder v7.0 software and validated by comparison with measured values. Simulations were then carried out considering three different scenarios: the current state without any HVAC system and two possible HVAC system configurations providing both air conditioning and ventilation. The results show that, from a comfort perspective, a lightweight radiant floor heating system, assisted by an appropriate ventilation system for air renewal placed at the floor level near the occupants, is preferable to floor-level fan coils and high ventilation channels. Furthermore, this was also confirmed by a preliminary energy analysis of the two HVAC options, where the ventilation effectiveness of the winter period, the temperature of the water the emitters are fed, the consequent COP value of the heat pump, and the electricity consumption were taken into consideration. [ABSTRACT FROM AUTHOR]

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

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises. In this paper the authors construct estimates of electricity consumption changes at the metropolitan level across the continental U.S., during the initial two months of the COVID-19 pandemic, demonstrating variations in electricity consumption patterns based on several factors. [ABSTRACT FROM AUTHOR]

Purpose: The reason for writing the paper is the fact that there is a global waste and resource crises that necessitate more sustainable waste management practices, comprising redirecting waste streams once sent to landfill or incinerated to be reused, recycled, or recovered instead. Blockchain waste management platform could be used by the waste management (WM) authorities to achieve sustainable sanitation. Design/methodology/approach: There are some fundamental issues that need to be resolved before Blockchain technology applications within the waste sector can have the desired effects for supporting sustainable waste management (e.g. lowering electricity consumption, increasing scalability of Blockchain). Findings: The focus tends to be on economic and often environmental benefits, but social benefits, and resultant equity and justice issues, are often overlooked. Practical implications: The application of Blockchain technology within the waste management sector is a particular example of supply chain management. Within the context of waste management, current applications of Blockchain typically focus on (1) payment or rewards facilitation and/or (2) monitoring and tracking of waste. Social implications: Compliance with the Directive 2008/98/EC of the European Parliament and of the Council on waste by reducing overall impacts of resource use and improving the efficiency of such use. Originality/value: For efficient results, it is crucial to introduce a modern waste management system to notice a full cycle of waste management, from how they were collected to when and where they were thrown away. Review of recent papers on these issues may be a valuable source of knowledge for interested parties. [ABSTRACT FROM AUTHOR]

This paper proposed and introduced an innovative concept of step-like server placement in data centers (DCs), replacing the traditional servers placed on the same vertical plane with step-like server placement. The studied simplified DC model was established based on an actual operating DC. The feasibility and reliability of the simplified model was validated by on-site experimental results. This paper is analyzed two scenarios: the effects of horizontal spacings (0, 0.01, 0.02, 0.03, and 0.04 m) between adjacent servers and with the effect of different supply air temperatures (SATs) (22.5, 23, 23.5, and 24°C) on the rack hotspot. The results show that step-like server placement can effectively enhance the thermal distribution, while the optimum thermal distribution is achieved with the spacing of 0.01 m in case 2. Under this circumstance, the rack hotspot reduced by 2.5°C, while case 2 is selected for further analysis in scenario 2. In scenario 2, the thermal environment in cases 6–8 are totally better than that in case 1 in terms of rack hotspot and temperature distribution. Although there appears another moderate heat accumulation at the top-side, the overall thermal distribution in the case with SAT of 24°C is still better than that in original case. The electricity consumptions of the CRACs can be saved by approximately 146 and 195 kWhr/day with the most optimal configuration. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

With the extensive growth of economy, the water system of the globe is under unprecedented pressure. So, the relationship between economic growth and water sustainability has been a common concern for the academia. The purpose of this paper is to use the bibliometric method to sort out the research progresses on economic growth and water sustainability during the past 30 years. More importantly, it tries to recognize the research hotspots and future trends in this field, to provide valuable directions for later research. A systemic review and bibliometric analysis are conducted by retrieving 1287 papers on water sustainability and economic growth. The results indicate that the number of articles related to the field of economic growth and water sustainability has increased steadily. The 1287 articles are published in 581 different journals, where the top 20 publishing journals have published 31.31% of them, and Sustainability and Journal of Cleaner Production have published the most retrieved articles. By introducing the Mapping Knowledge Domain with the combination of the software of COOC and VOSviewer, five clusters of research hotspots are summarized: economic growth and water consumption; water-energy-food-economy nexus and circular economy; climate change, water scarcity and water quality; water resources management and water footprint; wastewater management and valorization. [ABSTRACT FROM AUTHOR]

Blockchain technology brings about an opportunity to maintain decentralization in several applications, such as cryptocurrency. With the agents of a decentralized system operating independently, it calls for a consensus protocol that helps all nodes to agree on the state of the ledger. Most of the existing blockchains rely on Proof of Work (PoW) as the underlying consensus algorithm, resulting in a significant amount of electricity power consumption. Furthermore, it demands the miner to buy specific computation devices. Besides, a protocol to gather the society-related taxes such as public education funding and charities is lacking in existing consensus algorithms. In response, this paper proposes a new consensus algorithm, namely Proof of Humanity (PoH) aiming at gathering society-related taxes. According to PoH, the probability that an agent becomes a leader depends on its donations to non-profit accounts. Therefore, PoH encourages miners to donate money and gain mining power, its incentives, and transaction fees. The associated bureaucracy model is introduced briefly to address the required ecosystem for real case implementation of PoH. A distributed random variable generation algorithm is presented in this paper which ensures that the randomly selected leader is neither predictable nor adjustable. It is demonstrated that the proposed blockchain is totally robust against forking and possesses a high level of propagation speed, which ensures the scalability. Simulations show that the proposed blockchain network does not fail even in adverse scenarios where the majority of nodes refuse to propagate valid blocks. Besides, simulations reveal a suitable average block creation duration. [ABSTRACT FROM AUTHOR]

Limited by the influence of network topology and other factors, the theory of carbon emission flow is unreasonable in the allocation of carbon responsibility on the user side, which leads to the low enthusiasm of users to respond to emission mitigation. The emergence of bilateral transactions provides users with the freedom to choose the type of power supply which is of great significance to exploring the potential of users to reduce emissions and promote the consumption of new energy. For this reason, this paper proposes a bi-level emission reduction model of the hybrid electricity market considering carbon emission flow and source–load coordination. The upper level aims to maximize the revenue of wind, photovoltaic, and thermal power generators and establishes a market-clearing model based on the trading rules of the hybrid electricity market to obtain the bid-winning power of each generator and the hybrid market electricity price. After the market is cleared, the carbon emission liability of the user side is calculated by using the carbon emission flow theory. The lower level takes the minimum cost of electricity consumption as the target and uses electricity price and carbon responsibility as incentives to establish a decision-making model for users to purchase electricity and guide users to actively choose green energy for consumption. The results of the example show that compared with the single pool market trading model the carbon emissions of the system are reduced by 11.9% while the income of the new energy power generations is increased by 9.84% and the electricity cost of the user is reduced by 21.2%, which underlines a mutually beneficial outcome for all stakeholders in the market. [ABSTRACT FROM AUTHOR]

Renewable sources and Distributed Generation (DG) have been generating a growing economic interest given the increase in electricity consumption. For the end consumer, the lower environmental impact, easy-to-install and quick payback are great alternatives to traditional connections. DG growth drives new studies to predict different results in the electrical grid. The IEEE 1547 technical guidelines bring the possibility that in case of any failure that causes a shutdown, the operation is possible through intentional islanding provided by the electrical utility, continuing service, and maintaining customer satisfaction. For a more in-depth analysis of the impacts of this scenario, this paper contributes with a proposal to modify the strategy for identifying possible intentional islanding. A hybrid relay was modeled using passive techniques along with a suggestion for the operation of the newly formed Microgrid (MG), presenting a control philosophy of the regulators connected to the grid or being islanded, the latter defining the functions of the DGs as master–slave. The results obtained by the experiments proved to be excellent when inserting loads while the intentional islanding event occurs, confirming that the model is within the technical guidelines of IEEE 1547. This paper brings exciting conclusions about this operation, and the power quality of the electrical generator in the MG, according to the criteria established by ANEEL's Distribution Procedures Guidelines (PRODIST). [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term. [ABSTRACT FROM AUTHOR]

Solar-heating technology is a promising solution to help China achieve the "3060 double carbon" target as soon as possible. Seasonal thermal storage (STS) can effectively solve the mismatch problem of solar-heating systems between the supply and demand of thermal energy. Due to the instability of solar radiation resources and the heat demand, it is necessary to analyze the dynamic response characteristics and operation strategy optimization of the system in different operation stages. Yet, related studies are still scarce. The aim of this paper is to study the switching mechanism of the operation modes and the transitive relation of the system energy in different operation stages based on a pilot solar-heating system with STS in Huangdicheng, northern China. The impacts of different heating strategies on the system performance were also analyzed with a dynamic simulated method in TRNSYS. The results showed that the solar fraction of the system reached 89.4% in the third year, which was 3.6% higher than that in the first year. The quality–quantity heating operation strategies are effective ways to improve the discharge efficiency of the STS and the system performance without a heat pump. The electricity consumption of the pump on the heating side could be significantly reduced by 44.6% compared with the quality control. Ultimately, the findings in this paper are valuable for the optimization of the operation of solar-heating systems. [ABSTRACT FROM AUTHOR]

Summary: Potato is a tuber that, due to its great variety, is available in the markets all year round. Moreover, it is a widely consumed product worldwide on the account of its affordability and versatility. Therefore, studying it in depth is significant from not only a nutritional or economic point of view, but also for considering its potential environmental impact. In this paper, a Life Cycle Assessment (LCA) model is developed to assess the environmental impact of a model 5 kg bag of fresh washed potatoes. It considers all phases of potato processing, from the time the potato enters the facility until it leaves the gate (gate‐to‐gate LCA). The phase of the process with the greatest environmental impact is the cleaning and classification phase, mainly due to the significant consumption of water used in the cleaning process. The general consumption and storage phases are also relevant environmental impact phases to consider. In the case of these phases, the electricity consumption resulting from the operation of the facilities and product cooling is particularly noteworthy. After establishing the critical points, based on the environmental impact perspective, this paper proposes three measures to reduce the environmental impact of the process. The application of these three measures would reduce the environmental impact by 40% in environmental impact categories such as acidification or by 32% in the global warming category. [ABSTRACT FROM AUTHOR]

Presently there is a large consensus stating that while energy security remains key for assuring the consumption needs in Romania, its fulfilment may be achieved through two essential factors: the accessibility of resources and their long term availability. In the current geopolitical context, this paper aims to analyse Romania's energy security considering the aspects regarding the European Green Deal. Therefore, in the first part of the paper, there will be presented the general considerations regarding Romania's energy security and the possibility for Romania to become a regional energy security provider. In the second part of the paper, an analysis will be carried out on the functioning of the national system for the 2023-2024 winter, namely forecasts will be made on energy production and consumption while presenting the necessary measures for the proper functioning of economic operators' activities. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to comprehensively analyse the sustainability reporting practices of Australian electricity retailers in comparison with global sustainability reporting indicators outlined in the Global Reporting Initiative (GRI) framework. Design/methodology/approach: Based on the GRI G4 sector-specific guidelines, the paper investigated Australian electricity retailers' reporting in three broad areas of sustainability, namely, economic, environmental and social. The 2018/2019 annual reports along with websites, corporate social responsibility reports and standalone sustainability reports of the major electricity retailers listed on the Australian Energy Regulator were analysed and coded using a content-based technique. Findings: The findings inform that electricity retailers' disclosures are substantially varied between and within the three categories of sustainability reporting, and the majority of the retailers have failed to address over two-third of the GRI indicators. This study also shows that positive information is the dominant form of the disclosures, and reporting with declarative information without providing any quantifiable data is a common practice of the retailers who fail to address an indicator that requires information in numerical terms. Originality/value: Electric utilities provide essential services to society and have a significant influence on sustainable development. This study contributes to the social disclosure literature, in particular in a developed countries energy sector context, and captures insights about the sustainability reporting and accountability behaviour of the major electricity retailers operating in Australia. [ABSTRACT FROM AUTHOR]

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives. [ABSTRACT FROM AUTHOR]