Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 277, P. 123948 - 123948

Published: Aug. 29, 2020

Language: Английский

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Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea

Journal of Cleaner Production, Journal Year: 2019, Volume and Issue: 250, P. 119476 - 119476

Published: Nov. 27, 2019

Language: Английский

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Solar power generation forecasting using ensemble approach based on deep learning and statistical methods

Applied Computing and Informatics, Journal Year: 2019, Volume and Issue: 20(3/4), P. 231 - 250

Published: Nov. 6, 2019

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic generation depends heavily climate conditions, which fluctuate over time. In this research, we propose hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction solar from The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and newly proposed Auto-GRU. To enhance accuracy Machine Statistical Hybrid Model (MLSHM), employ two diversity techniques, i.e. structural data diversity. combine ensemble members in MLSHM, exploit four combining methods: simple averaging approach, weighted using linear approach non-linear combination through variance inverse approach. MLSHM scheme was validated real-time series datasets, sre Shagaya Kuwait Cocoa USA. experiments show all methods, achieved higher compared to traditional individual models. Results demonstrate outperformed only without method.

Language: Английский

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A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting

Energy Conversion and Management, Journal Year: 2020, Volume and Issue: 227, P. 113644 - 113644

Published: Nov. 20, 2020

Language: Английский

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Random forest solar power forecast based on classification optimization

Energy, Journal Year: 2019, Volume and Issue: 187, P. 115940 - 115940

Published: Aug. 12, 2019

Language: Английский

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