A review and taxonomy of wind and solar energy forecasting methods based on deep learning

Energy and AI, Journal Year: 2021, Volume and Issue: 4, P. 100060 - 100060

Published: March 7, 2021

Renewable energy is essential for planet sustainability. output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable vital ensure grid reliability permanency reduce the risk cost market Deep learning's recent success in many applications attracted researchers this field its promising potential manifested richness proposed methods increasing number publications. To facilitate further research development area, paper provides review deep learning-based solar wind published during last five years discussing extensively data datasets used reviewed works, pre-processing methods, deterministic probabilistic evaluation comparison methods. The core characteristics all works are summarised tabular forms enable methodological comparisons. current challenges future directions given. trends show that hybrid models most followed by Recurrent Neural Network including Long Short-Term Memory Gated Unit, third place Convolutional Networks. We also find multistep ahead gaining more attention. Moreover, we devise broad taxonomy using key insights gained from extensive review, believe will be understanding cutting-edge accelerating innovation field.

Language: Английский

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Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting

Applied Energy, Journal Year: 2021, Volume and Issue: 295, P. 117061 - 117061

Published: May 6, 2021

Language: Английский

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Hybrid deep neural model for hourly solar irradiance forecasting

Renewable Energy, Journal Year: 2021, Volume and Issue: 171, P. 1041 - 1060

Published: March 6, 2021

Language: Английский

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State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 225, P. 109792 - 109792

Published: Sept. 8, 2023

Language: Английский

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Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Frontiers in Earth Science, Journal Year: 2021, Volume and Issue: 9

Published: April 30, 2021

Solar radiation is the Earth’s primary source of energy and has an important role in surface balance, hydrological cycles, vegetation photosynthesis, weather climate extremes. The accurate prediction solar therefore very both industry research. We constructed 12 machine learning models to predict compare daily monthly values a stacking model using best these algorithms were developed radiation. results show that meteorological factors (such as sunshine duration, land temperature, visibility) are crucial models. Trend analysis between extreme temperatures amount showed importance compound events. gradient boosting regression tree (GBRT), lifting (XGBoost), Gaussian process (GPR), random forest performed better (poor) capabilities model, which included GBRT, XGBoost, GPR, models, than single but no advantage over XGBoost conclude

Language: Английский

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An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction

Energy, Journal Year: 2022, Volume and Issue: 254, P. 124250 - 124250

Published: May 14, 2022

Language: Английский

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Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad

Renewable Energy, Journal Year: 2022, Volume and Issue: 198, P. 51 - 60

Published: Aug. 10, 2022

Language: Английский

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Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization

Applied Energy, Journal Year: 2022, Volume and Issue: 311, P. 118674 - 118674

Published: Feb. 12, 2022

Language: Английский

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Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Sustainability, Journal Year: 2023, Volume and Issue: 15(9), P. 7087 - 7087

Published: April 23, 2023

This article presents a review of current advances and prospects in the field forecasting renewable energy generation using machine learning (ML) deep (DL) techniques. With increasing penetration sources (RES) into electricity grid, accurate their becomes crucial for efficient grid operation management. Traditional methods have limitations, thus ML DL algorithms gained popularity due to ability learn complex relationships from data provide predictions. paper reviews different approaches models that been used discusses strengths limitations. It also highlights challenges future research directions field, such as dealing with uncertainty variability generation, availability, model interpretability. Finally, this emphasizes importance developing robust enable integration RES facilitate transition towards sustainable future.

Language: Английский

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Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model

Energy, Journal Year: 2022, Volume and Issue: 262, P. 125592 - 125592

Published: Sept. 30, 2022

Language: Английский

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