1D Convolutional LSTM-based wind power prediction integrated with PkNN data imputation technique

Abstract

Various supervised machine-learning algorithms for wind power forecasting have been developed in recent years to manage wind power fluctuations and effectively correlate to energy consumption; Meanwhile, the performance of the model does suffer from missing values. To address the issue of missing values in wind power forecast, this paper proposes two methods: Clue-based missing at random (CMAR) and patterned k-nearest Neighbor (PkNN). In addition, a hybrid wind energy forecasting system has been created that is built on 1D-Convolutional neural networks, which are used to extract features from raw input, and Long Short-Term Memory, which employs time series data internal representation learning to improve the accuracy of month-wise wind power forecasting. The efficacy of the proposed model on generated datasets is also compared to the classic machine learning model to check the generalization ability. The strength of the Convolutional LSTM has been estimated in terms of different performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), R2, and explained variance score (EVS). The experimental results show that the use of the PkNN algorithm for data imputation; integrated with regression-based Convolutional LSTM is much more efficient in prediction over other deep neural network models.

Keywords

• Missing data imputation
• K values selection
• Meterological variables
• Patterned observation
• KNN
• LSTM