Novel wind speed forecasting model based on a deep learning combined strategy in urban energy systems

Expert Systems with Applications, Год журнала: 2023, Номер 219, С. 119636 - 119636

Опубликована: Фев. 2, 2023

Язык: Английский

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Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Energies, Год журнала: 2024, Номер 17(7), С. 1662 - 1662

Опубликована: Март 30, 2024

Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved accuracy may make it easier to deal with imbalances between generation consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, consumers manage resources effectively educated decisions about consumption, EF is essential. many applications, Energy Load Forecasting (ELF), Generation (EGF), grid stability, accurate crucial. The state of the art examined this literature review, emphasising cutting-edge techniques technologies their significance for industry. gives an overview statistical, Machine Learning (ML)-based, Deep (DL)-based methods ensembles that form basis EF. Various time-series are explored, including sequence-to-sequence, recursive, direct forecasting. Furthermore, evaluation criteria reported, namely, relative absolute metrics Mean Absolute Error (MAE), Root Square (RMSE), Percentage (MAPE), Coefficient Determination (R2), Variation (CVRMSE), well Execution Time (ET), which used gauge prediction accuracy. Finally, overall step-by-step standard methodology often utilised problems presented.

Язык: Английский

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A novel learning approach for short-term photovoltaic power forecasting - A review and case studies

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108502 - 108502

Опубликована: Апрель 29, 2024

Язык: Английский

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Short-term power load forecasting using bidirectional gated recurrent units-based adaptive stacked autoencoder

International Journal of Electrical Power & Energy Systems, Год журнала: 2025, Номер 165, С. 110459 - 110459

Опубликована: Янв. 23, 2025

Язык: Английский

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A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism

Applied Energy, Год журнала: 2022, Номер 327, С. 120063 - 120063

Опубликована: Окт. 7, 2022

Язык: Английский

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Energy generation forecasting: elevating performance with machine and deep learning

Computing, Год журнала: 2023, Номер 105(8), С. 1623 - 1645

Опубликована: Фев. 25, 2023

Abstract Distribution System Operators (DSOs) and Aggregators benefit from novel Energy Generation Forecasting (EGF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between production consumption. It also aids operations such as Demand Response (DR) management in Smart Grid architecture. This work aims develop test a new solution for EGF. combines various methodologies running EGF tests on historical data buildings. The experimentation yields different resolutions (15 min, one hour, day, etc.) while reporting errors. optimal technique should be relevant variety of applications trial-and-error manner, utilizing strategies, ensemble approaches, algorithms. final evaluation incorporates performance metrics coefficient determination ( $${R^{2}}$$ R 2 ), Mean Absolute Error (MAE), Squared (MSE) Root (RMSE), presenting comparative analysis results.

Язык: Английский

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Deep learning-driven hybrid model for short-term load forecasting and smart grid information management

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

Опубликована: Июнь 14, 2024

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, complexity and uncertainty load, along with large-scale high-dimensional energy information, present challenges in handling intricate dynamic features long-term dependencies. This paper proposes a computational approach to address these short-term information management, goal accurately predicting future demand. The study introduces hybrid method that combines multiple deep learning models, Gated Recurrent Unit (GRU) employed capture dependencies time series data, while Temporal Convolutional Network (TCN) efficiently learns patterns data. Additionally, attention mechanism incorporated automatically focus on input components most relevant prediction task, further enhancing model performance. According experimental evaluation conducted four public datasets, including GEFCom2014, proposed algorithm outperforms baseline models various metrics such as accuracy, efficiency, stability. Notably, GEFCom2014 dataset, FLOP reduced by over 48.8%, inference shortened more than 46.7%, MAPE improved 39%. significantly enhances reliability, stability, cost-effectiveness grids, which facilitates risk assessment optimization operational planning under context management grid systems.

Язык: Английский

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An integrated federated learning algorithm for short-term load forecasting

Electric Power Systems Research, Год журнала: 2022, Номер 214, С. 108830 - 108830

Опубликована: Окт. 10, 2022

Язык: Английский

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Interpretable fake news detection with topic and deep variational models

Online Social Networks and Media, Год журнала: 2023, Номер 36, С. 100249 - 100249

Опубликована: Май 22, 2023

Язык: Английский

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A new intelligent hybrid forecasting method for power load considering uncertainty

Knowledge-Based Systems, Год журнала: 2023, Номер 280, С. 111034 - 111034

Опубликована: Сен. 24, 2023

Язык: Английский

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