A machine learning approach for wind turbine power forecasting for maintenance planning

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 6, 2025

Abstract Integrating power forecasting with wind turbine maintenance planning enables an innovative, data-driven approach that maximizes energy output by predicting periods low production and aligning them schedules, improving operational efficiency. Recently, many countries have met renewable targets, primarily using solar, to promote sustainable growth reduce emissions. Forecasting is crucial for maintaining a stable reliable grid. As integration increases, precise electricity demand becomes essential at every system level. This study presents compares nine machine learning (ML) methods forecasting, Interpretable ML, Explainable Blackbox model. The interpretable ML includes Linear Regression (LR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Random Forest (RF); the explainable consists of graphical Neural network (GNN); blackbox model Multi-layer Perceptron (MLP), Recurrent Network (RNN), Gated Unit (GRU), Long Short-Term Memory (LSTM). These are applied EDP datasets three causal variable types: including temporal information, metrological curtailment information. Computational results show GNN-based outperforms other benchmark regarding accuracy. However, when considering computational resources such as memory processing time, XGBoost provides optimal results, offering faster reduced usage. Furthermore, we present various time windows horizons, ranging from 10 minutes day.

Language: Английский

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Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting

Expert Systems, Journal Year: 2025, Volume and Issue: 42(2)

Published: Jan. 8, 2025

ABSTRACT An accurate prediction of wind power generation is crucial for optimizing the integration energy into grid, ensuring reliability. This research focuses on enhancing accuracy forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized WRF forecast alongside ERA5 to estimate a farm located at Valladolid, Spain. The study evaluated performance ML based individually, as well combined model using inputs both datasets. hybrid resulted in 15% improvement root mean square error (RMSE) 10% increase compared standalone models, providing more reliable 1‐h generation. Additionally, availability over time was addressed: provides advantage projecting future, whereas offers retrospective data.

Language: Английский

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Optimizing deep neural network architectures for renewable energy forecasting

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 12, 2024

An accurate renewable energy output forecast is essential for efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), Convolutional Neural Network-LSTM(CNN-LSTM) Deep Network (DNN) topologies are tested solar wind production forecasting in this study. ARIMA was compared to the models. This study offers a unique architecture Networks (DNNs) that specifically tailored forecasting, optimizing accuracy by advanced hyperparameter tuning incorporation of meteorological temporal variables. The optimized LSTM model outperformed others, with MAE (0.08765), MSE (0.00876), RMSE (0.09363), MAPE (3.8765), R2 (0.99234) values. GRU, CNN-LSTM, BiLSTM models predicted well. Meteorological time-based factors enhanced accuracy. addition sun data improved its prediction. results show deep neural network can predict energy, highlighting importance carefully selecting characteristics fine-tuning model. work improves estimates promote more reliable environmentally sustainable electricity system.

Language: Английский

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Optimizing wind turbine integration in microgrids through enhanced multi-control of energy storage and micro-resources for enhanced stability

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 140965 - 140965

Published: Feb. 2, 2024

Language: Английский

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Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 24, P. 100772 - 100772

Published: Oct. 1, 2024

Language: Английский

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A Novel Wind Power Probabilistic Forecasting System Based on Transformer Networks and Multi-Objective Optimization

Published: Jan. 1, 2025

With the increasing capacity of grid-connected wind power systems, forecasting has become a major research problem in systems under background dual-carbon policy, and it is great practical significance to develop reliable methods. In order overcome difficulties data noise reduction, feature extraction uncertainty estimation, new system proposed. The improved variational mode decomposition algorithm used denoise data, overcoming subjective parameter selection traditional method. time convolutional network, Transformer bidirectional long short-term memory network are extract sequence features comprehensively ensure that local, long-term, considered simultaneously. multi-objective Bayesian optimization achieve Pareto optimal solution, quantile regression set for interval forecasting, so as systematically enhance model ability. performance evaluated based on two different datasets England, taking Penmanshiel farm an example, at confidence level 0.10, MAE RMSE values low 17.23 21.25 respectively, while WS value high 74.10%. experimental results show proposed better point ability than comparison model.

Language: Английский

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Assessing the availability and feasibility of renewable energy on the Great Barrier Reef-Australia

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 2035 - 2065

Published: Jan. 30, 2025

Language: Английский

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Feature fusion temporal convolution: Wind power forecasting with light hyperparameter optimization

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 2468 - 2481

Published: Feb. 8, 2025

Language: Английский

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Effects of electromagnetic radiation from offshore wind power on the physiology and behavior of two marine fishes

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 213, P. 117633 - 117633

Published: Feb. 8, 2025

Language: Английский

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A novel hybrid framework based on decomposition and Y-former model for accurate wind speed forecasting

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(3)

Published: April 14, 2025

Language: Английский

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