Hybrid boosting algorithms and artificial neural network for wind speed prediction

Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this poi...

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Vydané v:International journal of hydrogen energy Ročník 47; číslo 3; s. 1449 - 1460
Hlavní autori: Dosdoğru, Ayşe Tuğba, Boru İpek, Aslı
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 08.01.2022
Predmet:
Adaptive boosting
Artificial neural network
Extreme gradient boosting
Particle swarm optimization
Wind speed prediction
Adaptive boosting
Extreme gradient boosting
Wind speed prediction
Artificial neural network
Particle swarm optimization
ISSN:0360-3199, 1879-3487
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Popis
Shrnutí:Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction. •Due to the high volatile nature of wind speed, wind speed prediction is difficult.•Hybrid methods can be created by integrating a vast number of different methods.•Proposed hybrid methods give satisfactory results for wind speed prediction.•A promising wind speed prediction method with hybrid boosting algorithms is proposed.•Parameters of boosting algorithms and neural network are optimized by PSO.
ISSN:0360-3199
1879-3487
DOI:10.1016/j.ijhydene.2021.10.154