Machine Learning-based Time Series Modelling for Large-Scale Regional Wind Power Forecasting: a Case Study in Ontario, Canada

Abstract

Recently, time series forecasting has acquired considerable academic and industrial interests in various areas for different applications. Machine learning (ML) algorithms are known for their ability to capture the chaotic temporal non-linear relations in time series data. In this paper, various ML-based algorithms are employed and analyzed for time series forecasting of “regional wind power” in Ontario, Canada. To this end, the meteorological and spatial parameters with seasonal and temporal features are filtered and selected by a proposed deep feature selection approach. Then, multiple ML algorithms, including artificial neural network (ANN), deep neural network (DNN), long short-term memory (LSTM), bagging tree (BT), and support vector machine/regression (SVM/SVR), are used for training one-step ahead forecasting models. Finally, a comprehensive assessment of the constructed models is conducted based on different error criteria metrics. By evaluating and analyzing the performance of the models using testing data, it is observed that SVR/SVM is one of the most promising robust ML-based forecasting models. This technique results in reliable generic models that perform well with new data, where the testing MAPE % reaches a value of 13 %. Almost a similar MAPE is obtained from the ensemble modeling approach, which means combining process of the generated ML-based models does not significantly improve the predictions, in comparison with the developed SVR/SVM model. On the other hand, when constructing the multi-step ahead forecasting models, the predictions obtained from the multi-input multi-output (MIMO) LSTM approach are more reliable with higher accuracies. In other words, it is shown that the performance of the MIMO multi-step strategy is superior to the direct multi-step forecasting method, while employing algorithms with recursive properties.