Future Electricity Demand Research Articles

Forecasting Electricity Consumption Trends in India Using ARIMA-Based Regression Analysis: A Focus on Historical Lags

The ability to forecast electricity generation is crucial for effective energy management and policy planning. This study investigates the use of historical electricity generation data as a predictor for future generation trends using an autoregressive integrated moving average (ARIMA)-based regression model. Focusing on lagged values of electricity generation, we assess the predictive accuracy and statistical significance of the lagged variable (LAG1) for forecasting. The results indicate a strong positive relationship between past and future electricity generation, with the LAG1 coefficient being statistically significant at the 1% level. The regression model explains 97% of the variation in electricity generation, demonstrating its high utility for future forecasting. This analysis provides valuable insights for energy policymakers and stakeholders in preparing for future electricity demand.

Synergistic Emission Reduction of Carbon Dioxide and Atmospheric Pollutants Under Different Low-carbon Development Scenarios of the Power Industry in Jiangsu Province

The power industry is the main source of carbon dioxide （CO2） emissions in Jiangsu Province and also an important source of sulfur dioxide （SO2）, nitrogen oxides （NOx）, and particulate matter （PM）. In order to address climate change and contribute to the goal of "carbon peaking and carbon neutrality," Jiangsu Province has implemented a series of low-carbon development policies in the power industry. These policies not only reduce carbon emissions but also have important synergistic emission reduction benefits for atmospheric pollutants. Based on the low-carbon development plan for electricity in Jiangsu Province, a baseline scenario （BAU） and four low-carbon development scenarios have been constructed： current policy scenario （CLE）, IEA target scenario （IEA）, accelerated coal-fired power phaseout scenario 1 （STE1）, and scenario 2 （STE2）. An econometric model was used to predict the future electricity demand in Jiangsu Province, and the greenhouse gas-air pollution interactions and synergies （GAINS） model was employed to quantitatively analyze the impact of low-carbon policies in the power sector on the emissions of CO2, SO2, NOx, and PM, which are the major air pollutants in the region. The results showed that the electricity demand in Jiangsu Province has been increasing year by year, with an annual growth rate of approximately 4.01%. Under the BAU scenario, carbon emissions were projected to peak around 2030, with a peak carbon emission level of 462.03 Mt. Under the IEA scenario, it should reach its peak around 2028, with a peak emission level of 380.27 Mt. Under the CLE scenario, the peak would be expected to occur around 2026 at 353.46 Mt. In both STE1 and STE2 scenarios, carbon emissions had reached their peak and were continuously declining after 2020. In all scenarios, the replacement of conventional coal-fired power plants with natural gas （GAS）, nuclear power （NUC）, solar photovoltaic （SPV）, and wind power （WND） showed high synergistic benefits in pollution reduction and carbon reduction. The deployment of biomass energy （OS1） and non-renewable waste energy （OS2） will result in a significant increase in SO2 emissions. Carbon capture and storage （CCS） transformation of coal-fired power only showed significant synergistic benefits after 2035. The development of OS1 and OS2 fuel substitutes in power plants should focus more on reducing SO2 emissions, while upgrading and retrofitting CCS technology should prioritize the reduction of particulate matter emissions. The research findings provide a reference and decision-making basis for the synergistic efficiency of pollution reduction and carbon reduction in the power industry in Jiangsu Province.

Future Consumption Scenarios of Power Sector in Bangladesh

Future electricity demand is an essential prerequisite for planning the expansion of a power system. The purpose of this paper is to provide a forecast of electricity consumption in Bangladesh. Forecast is done here total as well as sector-wise consumption up to 2040 considering the base year 2009. The time series analysis to find the trend line by the method of Least Squares (LS) is applied in this study. We accumulated data from the annual reports of the Bangladesh Power Development Board for the years 2009 to 2023 and used that for forecasting purposes. For the convenient and policy purposes, the forecast is presented for every five years interval. The findings have significant implications with respect to energy conservation and economic development. As the study differentiated sector-wise projected growth of electricity consumption in Bangladesh, it has implications for the planners related to electricity distribution system. The findings can create awareness among the planners of power system expansion in Bangladesh to meet the continuous future development demand.

Empirical analysis of electricity demand in Albania. the impact in ecosystem

Albania represents the case of a developing country with a volatile demand for power facing an unstable domestic supply, especially during the last decade. Understanding future patterns of electricity usage is crucial in various planning contexts, the most important of which would be security of supply. In this framework, acknowledging the future national demand for electricity is needed for electricity providers for them to plan the sufficient and security of electricity supply. The aim of this paper is electricity demand modelling and forecasting in Albania. The model developed for the determinants of electricity demand for Albania allows understanding of the patterns of consumption behaviour. The main demand drivers of the electric consumption considered here are macroeconomic and demographic factors. The current work empirically estimates that the main determinants of electricity consumption in Albania for the period 1990-2014 are GDP, population and remittances. Results coefficients are then used to forecast electricity consumption for the period 2015-2030. We believe that the main contribution of the proposed estimation forecasts would be for correctly informing policy makers regarding electricity demand patterns in Albania and in forming expectations on a vital sector such as electricity.

A Hybrid Forecasting Model for Electricity Demand in Sustainable Power Systems Based on Support Vector Machine

Renewable energy sources, such as wind and solar power, are increasingly contributing to electricity systems. Participants in the energy market need to understand the future electricity demand in order to plan their purchasing and selling strategies. To forecast the electricity demand, this study proposes a hybrid forecasting model. The method uses Kalman filtering to eliminate noise from the electricity demand series. After decomposing the electricity demand using an empirical model, a support vector machine optimized by a genetic algorithm is employed for prediction. The performance of the proposed forecasting model was evaluated using actual electricity demand data from the Australian energy market. The simulation results indicate that the proposed model has the best forecasting capability, with a mean absolute percentage error of 0.25%. Accuracy improved by 74% compared to the Support Vector Machine (SVM) electricity demand forecasting model, by 73% when compared to the SVM with empirical mode decomposition, and by 51% when compared to the SVM with Kalman filtering for noise reduction. Additionally, compared to existing forecasting methods, this study’s accuracy surpasses LSTM by 63%, Transformer by 47%, and LSTM-Adaboost by 36%. The simulation of and comparison with existing forecasting methods validate the effectiveness of the proposed hybrid forecasting model, demonstrating its superior predictive capabilities.

The Economics of Electricity and Development: Planning for Growth and a Changing Climate

Many low- and middle-income countries have made tremendous gains in electrification over the past few decades. These improvements in electricity access have enabled a growing body of empirical evidence on its impacts. This article complements prior reviews on the impacts of electrification by addressing several major remaining challenges faced by the electricity sector in developing countries—impediments to maximizing electricity services᾽ economic effects, obstacles to recovering utility costs, difficulties in forecasting future electricity demand, and uncertainty regarding the future adoption of climate-mitigating technologies—and the existing micro economic causal evidence addressing those challenges. We describe how randomized experiments have complemented the quasi-experimental evidence and then highlight some remaining gaps in the existing literature. Specifically, we highlight climate adaptation within the electricity sector in developing countries, which remains a crucial gap in both the discussion on and financing of electrification for development. We use case studies of Nepal and Pakistan in South Asia—a region that both recently experienced great electrification gains and is among the most vulnerable to climate change—to illustrate the need for additional work on adaptation in the electricity sector. We conclude by linking to recent discussions on climate adaptation finance.

Pathway for a fully renewable power sector of Africa by 2050: Emphasising on flexible generation from biomass

The climate crisis coupled with energy poverty and injustice in Africa requires strategic measures to proffer sustainable solutions. A transition to a defossilised power sector is obtained for Africa by 2050, evolutionarily developed to fulfil the Paris Agreement. With the LUT Energy System Transition Model, the role of biomass in a least-cost power sector for Africa is investigated from 2020 to 2050 in five-year time steps. Techno-economic assumptions of employed technologies are applied. The study considers the current generation portfolio, lifespan of power plant technologies, and the current and future electricity demand to ascertain the best generation mix by 2050. Biomass potential of 334 TWh (17.5%) of the total African biomass potential of 1911 TWh is applied to the African power sector to develop two Best Policy Scenarios: a scenario without bioenergy (BPS-1) and a scenario with bioenergy (BPS-2). The results indicate a positive impact of bioenergy on the power sector: The total generation, total installed capacity, grid utilisation, levelised cost of electricity, and storage output decreased by 8.6%, 8.4%, 12.9%, 10.2%, 47.1% by 2050 respectively in BPS-2 relative to the BPS-1. In addition, flexible generation from biomass offers balancing to enhance variable renewable energy resources integration onto the power sector.

Cryptocarbon: How much is the corrective tax?

With increasing awareness of past environmental damage from crypto mining, questions arise as to how persistent the problem will be in the future and how taxation can help in addressing this negative externality. We estimate that the global demand for electricity by crypto miners reached that of Australia or Spain, resulting in 0.28% of global CO2 emissions in 2022. Projections suggest sustained future electricity demand and indicate further increases in CO2 emissions if crypto prices significantly increase and the energy efficiency of mining hardware is low. To address global warming damages, we estimate the corrective excise on the electricity used by crypto miners to be USD 0.045 per kWh, on average. Considering also air pollution costs raises the tax to USD 0.085 per kWh. Country-specific estimates vary depending on their electricity sources. Other new electricity reliant technologies are also driving up CO2 emissions. In our calculation, the corrective tax on electricity used by data centers is USD 0.048, on average.

A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

A novel method for long-term power demand prediction using enhanced data decomposition and neural network with integrated uncertainty analysis: A Cuba case study

This study developed a methodological approach for long-term electricity demand forecasting and applied it to the electricity demand in Cuba, which is crucial for transitioning from a fossil fuel-dependent system to renewable energy sources. The methodology employs enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) applied for obtaining long-term trends from historical electricity usage data decomposition, combined with a long short-term memory (LSTM) deep learning model for prediction. Comprehensive datasets, including historical electricity consumption, economic indicators, and demographic data, are utilized in the analysis. Monte Carlo simulations, then, are integrated to address uncertainties in prediction and explore 50 different scenarios of future electricity demand. The study forecasts varying scenarios for the energy demand of Cuba by 2050, with the extreme low scenario projecting a decrease of up to 7.9% compared to the 2019 level. This research offers a groundbreaking framework specifically designed to aid Cuba's energy sector stakeholders in informed decision-making during this critical energy transition. The adaptability of the methodology makes it applicable for long-term projections in various sectors, offering a reliable tool for global decision makers.

Improving Electricity Theft Detection Using Electricity Information Collection System and Customers’ Consumption Patterns

Electricity theft detection (ETD) techniques employed to identify fraudulent consumers often fail to accurately pinpoint electricity thieves in real time. The patterns associated with electricity use are leveraged to identify anomalies indicative of electricity theft. However, challenges in the benchmark ETD include overfitting and a high incidence of false positives (FPs) resulting from incorrect usage patterns formed by considering only electricity consumption patterns without accounting for external factors that contribute to variations in normal consumption patterns. Further investigation is required to precisely detect instances of electricity theft, thereby minimizing nontechnical losses and forecasting future electricity demand to maintain a stable supply. This study employs a master energy meter located on the transformer side to monitor the amount of energy distributed to the region. Zones with a high likelihood of energy theft are detected by comparing the sum of readings from all the smart meters with the readings from the master energy meter installed on the HV of the substation transformer within the same electric feeder. Ensemble XGBoost machine-learning algorithm and K-Means algorithm are used for the classification of malicious and nonmalicious samples and grouping similar types of consumers together, respectively. This makes the proposed model resistant to false-positive rates caused by changes in usage patterns that aren’t done on purpose. Furthermore, energy thieves are identified by detecting anomalies in consumption behavior while maintaining constant internal and external environmental variables. This novel model proposed here mitigates the FP rate found in the present research of electricity usage data. Our approach outperforms support vector machines, convolution neural network, and logistic regression in simulations. Precision, F1-score, recall, Matthews Correlation Coefficient, Receiver Operating Characteristics (ROC)-Area Under The Curve (AUC), and Precision Recal (PR)-Area Under The Curve (AUC) validate our model. The simulation results show that the proposed K-Means-LSTM-XGBoost model improved the classifier’s F1-score, precision, and recall to 93.75%, 95.16%, and 92.38%, respectively. Our model classifies huge time series data with high precision and can be utilized by the utility for real time electricity theft detection.

Techno-economic analysis of a hybrid photovoltaic-wind-biomass-battery system for off-grid power in rural Guatemala

Guatemala has made significant progress in improving its electrical infrastructure in recent years. However, most studies and efforts have focused on developing policies that directly benefit the national electrical market, which may cause a lack of attention toward solutions that cater to low-consumption cases, such as residential and rural communities. Therefore, it is vital to consider the needs of these communities while developing policies and solutions to ensure that they also have access to reliable and affordable sources of electricity. This study analyzes the cost-effectiveness and technical performance of a hybrid renewable energy system (HRES) that can meet the power needs of low electricity-consuming households in a rural region of Guatemala. The proposed HRES comprises a hybrid photovoltaic-wind turbine-bio generator coupled to battery storage, which caters to the energy needs of a typical household in Alta Verapaz, a rural area in Guatemala with limited electricity access (64.61%). The research considers three scenarios: I) basic electricity needs for the household, II) increased electricity needs for cooking and water heating, and III) future electricity demand in 2050, considering the role of the renewable energy market. Based on Scenario I, the cost-effective solution is a PV system with a capacity of 5.39 kW and 29 kWh battery capacity, with a cost of energy (COE) of 0.893 $/kWh. In Scenario II, a hybrid solution consisting of a 2.46 kW PV system, a 2.20 kW bio-generator, and 16 kWh battery capacity o, results in a COE of 0.605 $/kWh. Scenario III suggests a hybrid system, including 7.90 kW of PV, 3.30 kW bio-generator, and 14 kWh battery to meet the expected energy demand in 2050. COE for this solution is estimated to be 0.297 $/kWh. Considering the declining costs of renewable energy technologies by 2050, the findings highlight that the proposed HRES can be an affordable solution for low-consumption scenarios such as off-grid areas in Guatemala.

A novel outlier calendrical heterogeneity reconstruction deep learning model for electricity demand forecasting

The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers.

Temperature sensitive electricity demand and policy implications for energy transition: a case study of Florida, USA

The demand for electricity is soaring, propelled not only by population and GDP growth but also the pressing effects of climate change. This study seeks to address the uncertainties surrounding future electricity demand by projecting monthly consumption in Florida, USA, taking into account diverse climate scenarios and their potential impacts. Our approach involves utilizing the degree-day method and constructing an energy consumption regression model grounded in historical data. Key variables, including population, employment, GDP, electricity prices, temperature, and daylight hours, are systematically analyzed. This model acts as the fundamental basis for forecasting future electricity needs in residential, commercial, and industrial sectors across the state of Florida up to the year 2050, considering different climate scenarios. Under the Representative Concentration Pathway (RCP) 4.5 scenario, the residential sector foresees a substantial 63% increase in electricity demand from 2001–2019 to 2050. Under the more extreme RCP 8.5 scenario, this surge climbs to 65%. Meanwhile, the commercial and industrial sectors are expected to witness a 47% and 54% upswing in demand under RCP 4.5 and RCP 8.5, respectively. Intriguingly, heightened demand for cooling during scorching summers outweighs the reduced need for heating in winter, particularly in the residential sector. The current renewable energy policies fall short of addressing the impending climate-driven surge in electricity demand. To combat this, our recommendation is the implementation of a Renewable Portfolio Standard, aimed at significantly enhancing the proportion of renewables in Florida's electricity mix. This paper concludes with a set of crucial policy recommendations, imperative for steering a sustainable transition to renewable energy and effectively managing the impacts of extreme heat on people's lives. These recommendations serve as a strategic roadmap for navigating the evolving landscape of electricity demand amidst the complex challenges posed by climate change.

Public support and opposition toward floating offshore wind power development in Norway

For countries like Norway, with abundant offshore wind resources and deep seas, floating wind power technology can play an essential role in the green energy transition. However, this technology is still immature, and the first utility-scale floating offshore wind power projects need substantial support for technology development to be commercially feasible. This study employs an online survey targeting the general population in Norway (N = 1011) to investigate support and opposition toward floating offshore wind power development. The survey includes a discrete choice experiment focusing on policy-relevant factors such as the export of electricity, reducing domestic carbon emissions by electrifying offshore oil and gas platforms, impact on global technology cost trends, and involving domestic offshore industries as key players in the floating offshore wind sector. We find the highest support for developing projects that utilize technology from domestic offshore industries and projects connected to the domestic electricity grid. Projects aimed at reducing domestic carbon emissions by electrifying offshore oil and gas platforms are favored over those for exporting electricity to other countries. A significant impact on future technology costs does not lead to increased support for the project. Projects presented after a framing text focusing on meeting future electricity demand result in a higher willingness to pay for floating offshore wind projects than those presented after a framing text focusing on meeting climate objectives. Respondents opposing all the projects are likely climate skeptics and believe that project developers should bear all the project costs. Norway is expected to play a critical role in developing floating wind power. However, the Norwegians demand clear national benefits to be willing to shoulder the cost of spearheading the floating offshore wind power development. Understanding these preferences is vital for crafting energy policies aligning with public interests and rapidly integrating floating wind power into the green energy transition.

Study on UPQC Integration Benefits in a Hybrid Solar Wind Energy System

The researchers are focusing on harnessing the electricity form the renewable energy resources by overcoming the challenges faced by them due its variable nature. This study discusses the developing significance of renewable energy sources (RES), in particular wind and solar electricity, in meeting future electricity demands. India’s geographical length is highlighted as beneficial for integrating variable renewable energy outputs into its grid. The paper also addresses Power-quality (PQ) issues springing up from integrating renewable energy sources, proposing solutions including single and Unified active power filter (SUAPF) and distributed Static Compensators (DSTATCOM). The work examines power quality issues associated with grid-related solar PV systems and the combination of Unified Power Quality Conditioners (UPQCs) in hybrid solar-wind systems. The analysis of the UPQC integration with the solar wind energy based hybris system concludes the enhancement in the power quality issues such as total harmonic distortions along with balancing the reactive power in the line.

Global transcontinental power pools for low-carbon electricity

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

Reliability assessment of the ocean thermal energy conversion systems through Monte Carlo simulation considering outside temperature variation

AbstractThe ocean thermal energy conversion (OTEC) systems, as renewable energy-based power plants, have the potential to play a significant role in meeting future electricity demands due to the vast expanse of the world's oceans. These systems employ the temperature difference between surface ocean waters and deep ocean waters to drive a thermodynamic cycle and produce electricity. The temperature of deep ocean waters, approximately 1000 m below the surface, is approximately 4 °C, while surface ocean temperatures typically range between 20 and 30 °C. The generated power of OTEC systems is dependent on these temperature differences and may vary with changes in surface ocean temperatures. In this study, the main focus is to find the impact of temperature variation on the failure rates of OTEC system components and the generated power output of these plants. The findings indicate that as the demand for the power system increases, its reliability decreases. In order to improve the reliability of the power system, the integration of a new generation unit, such as the close cycle OTEC power plant under investigation, could be necessary. The findings also indicate the importance of considering temperature variation in the evaluation of the reliability of such types of power plants based on renewable energy.

Contemporary climate analogs project strong regional differences in the future water and electricity demand across US cities

Contemporary climate analogs project strong regional differences in the future water and electricity demand across US cities

Forecasting sustainable power generation profiles to achieve net zero emissions using multi-objective techno-ecological framework: A study in the context of India

Climate change is becoming the most pronounced issue worldwide, with its origins dating back to the time of industrialization. It has now become imperative to take remedial steps to mitigate its harmful effects throughout the globe. In today's era, climate change is expected to worsen due to air emissions from power generation systems as they primarily depend on coal and fossil fuels. Moreover, as the global population increases, the demand for electricity will rise, further exacerbating the problem. Given this situation, populous nations like India and China are seen as key players in achieving net-zero emissions worldwide by transitioning to sustainable power generation. This study presents a multi-objective optimization method to meet the future electricity demand while ensuring net-zero emissions by mitigating pollution through a techno-ecological synergy (TES). In turn, this study incorporates the ecosystem as a process system, unlike the conventional decision-making methods that ignore the ecosystem's ability to mitigate pollution. As a case study, Delhi, the capital of India, which is known as one of the world's most polluted cities, is selected to showcase the proposed methodology. The study proposes various scenarios for sustainable power generation for Delhi to achieve net-zero emissions. The findings reveal that solar energy could be a viable alternative to conventional fossil fuels to meet Delhi's energy demand by 2050. This is achievable by implementing an energy transition plan that involves the aggressive phasing out of the coal power plant, which aligns with the urgent requirement to reduce emissions.