Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

Renewable Energy, Journal Year: 2024, Volume and Issue: 226, P. 120437 - 120437

Published: April 1, 2024

Language: Английский

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Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 115, P. 109116 - 109116

Published: Feb. 15, 2024

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129716 - 129716

Published: Nov. 21, 2023

Language: Английский

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Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108435 - 108435

Published: April 25, 2024

Language: Английский

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Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 157, P. 111543 - 111543

Published: March 29, 2024

Language: Английский

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Wind and Solar Power Generation Forecasting Based on Hybrid CNN-ABiLSTM, CNN-Transformer-MLP Models

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 122055 - 122055

Published: Nov. 1, 2024

Language: Английский

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A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Electronics, Journal Year: 2024, Volume and Issue: 13(10), P. 1837 - 1837

Published: May 9, 2024

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate inherent variability of solar generation. We propose novel model that combines improved variational mode decomposition (IVMD) with temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, proposed IVMD-TCN-GRU framework targets significant research gap methodologies. Initially, leveraging sparrow search algorithm (SSA), we optimize parameters VMD, including component K-value and penalty factor, based minimum envelope entropy principle. The optimized VMD then decomposes power, while TCN-GRU harnesses TCN’s proficiency learning local features GRU’s capability rapidly modeling sequence data, better utilize global correlation information within data. Through this design, adeptly captures correlations time series demonstrating superior performance prediction tasks. Subsequently, SSA is employed GRU parameters, decomposed components feature attributes are inputted neural network. This facilitates dynamic multivariate sequences. Finally, predicted values each summed realize forecasting. Validation using real data from station corroborates demonstrates substantial reduction RMSE MAE up 55.1% 54.5%, respectively, particularly evident instances pronounced photovoltaic fluctuations during inclement weather conditions. method exhibits marked improvements accuracy compared traditional methods, underscoring its significance enhancing precision ensuring secure scheduling stable operation systems.

Language: Английский

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Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134545 - 134545

Published: Jan. 1, 2025

Language: Английский

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Electrical load forecasting based on variable T-distribution and dual attention mechanism

Energy, Journal Year: 2023, Volume and Issue: 283, P. 128569 - 128569

Published: July 28, 2023

Language: Английский

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