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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]

Precise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand. [ABSTRACT FROM AUTHOR]

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

With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (PSO) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved PSO and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user's lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation. [ABSTRACT FROM AUTHOR]

The general development of technology and universal access means that gastronomy furnishings can be found in every corner of the world. Therefore, it is important to develop these devices and the machines that constitute them. We are talking about refrigeration systems located inside gastronomy furnishings. The R404A refrigerant, popular in recent years, is being withdrawn from use due to its harmful impact on the environment. Modern synthetic refrigerants or natural refrigerants can be used as alternative substances. In modern solutions, it is expected that devices and all cooperating elements have the lowest possible harmful impact on the environment and the user while, at the same time, having the highest possible energy efficiency. First, tests were carried out with the R404A refrigerant. Then, the working medium was replaced without changing any element of the refrigeration system with the modern R455A refrigerant. The system was tested in terms of the operating parameters achieved and in terms of electricity consumption. It was found that there is an alternative R455A refrigerant operating in the refrigeration system of catering furnishings, which provides the system with an average of 34% better energy efficiency than the reference refrigerant R404A. It was also found that the time needed to achieve the set working conditions decreased. An alternative refrigerant allows a refrigeration system to be built based on components available on the market or one that can be used as a direct replacement for the old refrigerant. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

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

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

Load forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [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]

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver's blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030. This implies the need to more than double the existing wind and PV capacity at the national level, which in turn introduces numerous challenges. Amongst them, spatial planning for new RES installations seems to be the most demanding, with the adoption of novel technological solutions in the field of RES potentially holding a key role. New technologies, like offshore wind and floating solar, are gradually gaining maturity and may offer such an alternative, challenged at the same time however by the need to entail minimum disruption for local ecosystems. To that end, we currently assess the theoretical potential of floating PVs for lentic water systems of the Greek mainland. We do so by looking into 53 different lentic water systems across the Greek territory that meet the constraint of 1 km2 minimum surface area, and we proceed with the estimation of the relevant floating PV capacity per system under the application of a minimal intervention approach, assuming PV coverage of 1% over the total lentic water system area. In this context, our findings indicate a maximum, aggregate theoretical capacity that could exceed 2 GWp at the national level, with the respective annual energy yield reaching approximately 4 TWh or, equivalently, >6% of the country's anticipated annual electricity consumption in 2030. Finally, our results extend further, offering a regional level analysis and a set of policy directions and considerations on the development of floating solar in Greece, while also designating the energy merits of floating PVs against similar, land-based installations. [ABSTRACT FROM AUTHOR]

In the wake of the dual-carbon objective, the call for low-carbon attributes in integrated energy systems is ascending, with an amplified imperative to integrate wind and solar power efficiently. This study introduces an advanced low-carbon optimization framework for integrated energy systems, incorporating a sophisticated time-differentiated carbon accounting mechanism attentive to consumer emissions. A nuanced carbon accounting model is crafted to assess consumer emissions with greater accuracy. Predicated on these emissions, a refined low-carbon demand response model is articulated, factoring in the influence of carbon emission factors pertinent to electricity and heat procurement on user conduct. This model integrates the consideration of heat reclaimed from methanation processes, which in turn informs the carbon emission factors associated with purchased heat, and evaluates the subsequent optimization impact on the system. The proposed model is designed to curtail the system's operational expenditures and is operationalized via the CPLEX solver. Through the establishment of various scenarios for evaluative comparison, the model is corroborated to substantially augment the system's proficiency in assimilating wind and solar energy, markedly curtail carbon emissions, and facilitate a sustainable and cost-efficient operation of the integrated energy system. [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]

This article aims to demonstrate the technical capabilities and effectiveness of an energy production and management system for school facilities using a modular solution. The system is assumed to generate electricity from renewable sources, such as wind or sun. The potential of renewable energy sources in Cracow, Poland, was assessed, with a focus on solar energy (photovoltaic panels, PV). Taking into account the installation of heating and other equipment, an analysis of the facility's electricity demand was carried out. The study recommended the use of a heat pump system to heat and cool the facility. Renewable energy sources will meet 81% of the facility's projected annual demand, according to the study. An analysis of the energy consumption and production profiles shows that almost 69% of the energy produced by the PV panels is consumed on site. Of the remaining energy, 31% is fed back into the grid and sold to the grid operator or used by other facilities within the shared settlement. The overall balance results in a small electricity deficit that must be covered by the grid. If suitable sites are available, the facilities under study could consider installing a wind turbine as a potential supplement to the energy deficit. [ABSTRACT FROM AUTHOR]

We construct a sustainable development model by examining the short‐run relationship, long‐run relationship and causality between renewable energy consumption and economic growth. We consider a sample of 142 countries for 1991–2019 and employ time series analysis. The study noticed the long‐run and short‐run relationship between economic growth and renewable energy consumption in the case of all Countries Groups and regions such as Europe and Central Asia, Middle East and North Africa, Sub‐Saharan Africa and low‐income economies. Overall, we conclude that renewable energy consumption positively influences economic growth, which can motivate countries to achieve sustainable development goals. A nation cannot only focus on economic development when growing environmental and climatic concerns exist. What is essential is the prioritisation of sustainable development goals. The constructed model will help the countries plan, modify and implement national and international sustainable development policies. The results will also be helpful to intergovernmental organisations for channelling countries' efforts towards sustainable development. [ABSTRACT FROM AUTHOR]

The cost of manufacturing and electricity consumption are key considerations in encouraging the adoption of solar air heaters (SAH) in sunny and low‐income areas. These parameters can have a significant impact on promoting the use of these devices. In this study, solar air heating was designed with the objective of achieving the highest possible efficiency/cost ratio. It is a corrugated channel SAH whose structure is equipped with two barriers perforated with a sufficient number of holes for a good airflow distribution. A new model was developed to evaluate the qualitative parameters that describe the thermos‐energetic behavior of a heating system. These parameters were measured using experimental data obtained under real operating conditions. The thermal model assumes a uniform temperature for the glass, absorber, and insulation of the collector, while the temperature of the circulating air is assumed to vary linearly along the collector. To ensure that these assumptions were valid, the collector was cut into a number of 0.1 m sections in the direction of flow. By comparing the numerical results with the experimental data, the model was validated and then used to calculate the temperature profiles of the different elements of the collector, as well as to estimate the impact of certain operational parameters on its thermal performance. Relative percentage error values, between the numerical and the experimental results, of 1.7517%, 1.0750%, 0.8577%, 2.2371%, and 2.3637% for absorber plate temperature, outlet airflow temperature, useful power, thermal, and effective efficiencies, respectively, are recorded. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond. [ABSTRACT FROM AUTHOR]

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

We investigate the power system and climate policy implications of the anticipated expansion in electricity demand from datacentres. We perform a joint optimization of Generation and Transmission Expansion Planning for the island of Ireland considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system. A renewable energy target is more costly than a technology-neutral carbon reduction policy due to the restriction to invest solely in renewable energy, and the divergence in costs increases non-linearly in electricity demand. A carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. The increased demand from datacentres, which is relatively constant, is best matched by a power supply with constant output, generally fossil-fuelled power plants, potentially in combination with carbon capture and sequestration technology, than variable supply. Datacentre demand increases power system costs by 6% under a carbon reduction policy, and by 9% to 15% under renewable energy targets. However, the abatement cost of carbon under renewable targets decreases at higher levels of demand, while the opposite is true for the abatement cost under technology-neutral targets. High renewable targets crowd out other low-carbon options, namely CCS, leading to a higher cost solution for the same reduction in emissions, although this result is sensitive to increases in fossil fuel prices. The results suggest that climate-related energy policy should focus on technology-neutral carbon reduction policies. The climate impacts of datacentres on energy demand and thus supply requirements should also feature in climate and energy policy discussions. Climate policy that focuses on renewable energy only is more expensive than a technology-neutral climate policy The costs of a renewable-only focused climate policy increase non-linearly as demand increases [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]

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]

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]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

This study investigated the impact of information and communication technology (ICT) on electricity intensity, incorporating electricity prices, financial development, and population growth in Korea from 1990 to 2020, using the ARDL (autoregressive distributed lag) model. Three cases were considered, each relating to a different ICT proxy: Internet use, mobile cellular phone use, and exports of ICT-related products. The results varied depending on the proxy used to represent ICT. An increase in mobile cellular phone use leads to an increase in electricity intensity in the long run; however, the short-run effects of this change are unclear. An increase in Internet use also leads to an increase in electricity intensity in the long run but induces a decrease in electricity intensity in the short run. Increments in the exports of ICT-related products lead to an increase in electricity intensity in the short run; however, this effect is negligible in the long run. Electricity prices do not affect electricity intensity in all cases, and financial development reduces the intensity of electricity in the cases of the use of both mobile cellular phones and the Internet as proxies for ICT, whereas population growth increases electricity intensity in all cases. [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]

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users' satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model. [ABSTRACT FROM AUTHOR]

Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2${\rm {CO}}_{2}$ emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances. [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]

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant. [ABSTRACT FROM AUTHOR]

Electricity consumption of mining enterprises depends on many factors. The influence of factors on the process of electricity consumption is complex and diverse, and it is impossible to describe it within the framework of deterministic methods, due to the uncertainty of the conditions that determine the influence of factors. These factors include mining geological, mining technological, climate-meteorological, energetic, organizational, operational and others. Energy consumption regimes of mining enterprises depend on many mining technological factors, most of which are insufficiently taken into account in the analysis and planning of electricity consumption. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the nonlinear relationship between ICT and CO2 emissions by controlling for economic growth, foreign direct investment, energy consumption, and trade openness. Using data from 16 Middle East and North African (MENA) countries over the period 1990–2019, we apply the Panel Smooth Transition Regression (PSTR) model, as introduced by (González A, Teräsvirta T, vanDijk D (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604), to study the potential regime-switching behavior of the relationship between the variables. The results reveal the existence of a strong regime-switching effect between ICT and CO2 emissions. It was found that after reaching a certain threshold, ICT use and penetration starts to significantly mitigate environmental degradation. Our results show that high levels of ICT not only improve environmental quality but can also be part of the solution to combat the environmental challenges that the MENA region has faced over the past decades. In addition, to account for the potential endogeneity bias, we also develop and estimate a PSTR model with instrumental variables (IV-PSTR) using the approach of (Fouquau et al., Econ Model 25:284–299, 2008). The results obtained confirm those initially found by the PSTR model. The study concludes with policy implications. [ABSTRACT FROM AUTHOR]