An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms

Energy, Год журнала: 2024, Номер 296, С. 131259 - 131259

Опубликована: Апрель 9, 2024

Язык: Английский

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Power consumption prediction for electric vehicle charging stations and forecasting income

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 18, 2024

Abstract Electric vehicles ( EVs) are the future of automobile industry, as they produce zero emissions and address environmental health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, demand for power consumption forecasting is increasing to manage charging stations effectively. Predicting can help optimize operations, prevent grid overloading, outages, assist companies in estimating number required meet demand. The paper uses three time series models predict electricity stations, SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms ARMA (Auto ARIMA models, with least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) MAPE Percentage scores revenue. data used validation consists activities over a four-year period from public outlets Colorado, six months ChargeMOD's terminals Kerala, India. Power usage also forecasted based on wheels vehicles, finally, plan subscription same source utilized anticipate income, that helps develop pricing strategies maximize profits while remaining competitive. Utility firms networks may use accurate forecasts variety purposes, such scheduling determining expected energy requirements stations. Ultimately, precise effective planning design EV infrastructure. main aim this study create good which estimate electric vehicle verify if firm has income along some accuracy measures. results show plays vital role providing us information. According here, four wheelers than two wheelers. Also, DC facility AC These be determine cost operate EVs its subscriptions.

Язык: Английский

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Minute-level ultra-short-term power load forecasting based on time series data features

Applied Energy, Год журнала: 2024, Номер 372, С. 123801 - 123801

Опубликована: Июль 3, 2024

Electricity is fundamental to the development of national economies and societies, reliant on accurate power load forecasting for its stable supply. Ultra-short-term analyzes historical data predict changes within next hour. This crucial achieving efficient dispatching, improving emergency management, ensuring operation system. However, with increasingly widespread application renewable energy, inherent intermittency exacerbates complexity randomness loads, posing a challenge models accurately capture features. In addressing this challenge, study presents novel method feature extraction from time series data, aimed at enhancing accuracy forecasting. By analyzing trend, periodicities, randomness, it simplifies complex into several features, significantly reducing noise-induced errors identification understanding Moreover, applies five prevalent deep learning models. Experimental results show that using reduces mean absolute percentage error by an average 54.6905%, 42.6654%, 51.3868% datasets three different substations in China. These not only affirm method's efficacy but also provide new technical foundations reliable functioning future systems.

Язык: Английский

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Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models

Energy, Год журнала: 2023, Номер 286, С. 129566 - 129566

Опубликована: Ноя. 4, 2023

Язык: Английский

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Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

Sustainability, Год журнала: 2024, Номер 16(7), С. 2958 - 2958

Опубликована: Апрель 2, 2024

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance precision in dynamic energy landscape reveals promising outcomes employing a robust methodology involving model application large amount data. demonstrates accurate predictions, as evidenced low Sum Squared Errors (SSE) 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) test (2.44%) datasets. recommends adoption SARIMA models, supported MAPE values, influence technology future-proof decision-making. highlights societal implications informed planning, including enhanced sustainability, cost savings, improved resource allocation for communities industries. synthesis analysis, technological integration, consumer-centric approaches marks significant stride toward resilient efficient ecosystem. Decision-makers, stakeholders, researchers may leverage findings sustainable, adaptive, positioning sector address evolving challenges effectively empowering consumers while maintaining efficiency.

Язык: Английский

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Improving electricity demand forecasting accuracy: a novel grey-genetic programming approach using GMC(1,N) and residual sign estimation

Grey Systems Theory and Application, Год журнала: 2024, Номер 14(4), С. 708 - 732

Опубликована: Май 29, 2024

Purpose This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance predictive performance of grey models by proposing a novel multivariate convolution model incorporating residual modification and genetic programming sign estimation. Design/methodology/approach research begins constructing demonstrates utilization prediction accuracy exploiting signs forecast residuals. Various statistical criteria are employed assess proposed model. validation process involves applying real datasets spanning from 2001 2019 for annual in Cameroon. Findings hybrid outperforms both non-grey consumption. model's is evaluated MAE, MSD, RMSE, R 2 , yielding values 0.014, 101.01, 10.05, 99% respectively. Results cases real-world scenarios demonstrate feasibility effectiveness combination offers significant improvement over competing models. Notably, dynamic adaptability enhances mimicking expert systems' knowledge decision-making, allowing identification subtle changes demand patterns. Originality/value introduces that incorporates application leveraging residuals represents unique approach. showcases superiority existing models, emphasizing its expert-like ability learn refine rules dynamically. potential extension other fields also highlighted, indicating versatility applicability beyond

Язык: Английский

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A novel hybrid model based on modal decomposition and error correction for building energy consumption prediction

Energy, Год журнала: 2024, Номер 294, С. 130811 - 130811

Опубликована: Фев. 26, 2024

Язык: Английский

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Hypertuned wavelet convolutional neural network with long short-term memory for time series forecasting in hydroelectric power plants

Energy, Год журнала: 2024, Номер unknown, С. 133918 - 133918

Опубликована: Ноя. 1, 2024

Язык: Английский

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A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast

Energy, Год журнала: 2024, Номер 296, С. 131065 - 131065

Опубликована: Март 26, 2024

Язык: Английский

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Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035

Energy, Год журнала: 2024, Номер 302, С. 131949 - 131949

Опубликована: Июнь 5, 2024

Язык: Английский

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