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• W2892088821 abstract "The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model." @default.
• W2892088821 created "2018-09-27" @default.
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• W2892088821 date "2018-01-01" @default.
• W2892088821 modified "2023-09-28" @default.
• W2892088821 title "Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model" @default.
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• W2892088821 doi "https://doi.org/10.4018/978-1-5225-4766-2.ch015" @default.
• W2892088821 hasPublicationYear "2018" @default.
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