Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine

Tamer Khatib, Rezeq Direya and Asma Isead

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract: In this research, an approach for predicting wind energy in the long term has been developed. The aim of this prediction is to generate wind energy profiles for four cities in Palestine based on wind energy profile of another fifth city. Thus, wind energy data for four cities, namely, Nablus city, are used to develop the model; meanwhile, wind energy data for Hebron, Jenin, Ramallah, and Jericho cities are predicted based on that. Three machine learning algorithms are used in this research, namely, Cascade-forward neural network, random forests, and support vector machines. The developed models have two input variables which are daily average cubic wind speed and the standard deviation, while the target is daily wind energy. The R-squared values for the developed Cascade-forward neural network, random forests, and support vector machines models are found to be 0.9996, 0.9901, and 0.9991, respectively. Meanwhile, RMSE values for the developed models are found to be 41.1659 kWh, 68.4101 kWh, and 205.10 kWh, respectively.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8303152

DOI: 10.1155/2020/8303152

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