An Aggregative Machine Learning Approach for Output Power Prediction of Wind Turbines

Accurately forecasting power output of renewable sources is a necessity in operation of today's grid in order to achieve optimal energy utilization and carbon-free ecosystem. This study devises a stable, effective and accurate model for dayahead prediction of wind turbine power output through use of an aggregative approach. The method involves two types of Artificial Neural Network (Radial Basis and Conventional Feedforward Networks), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) techniques. It is targeted at comparing the prediction models for their individual performances and finally coming upon an aggregative approach which outperforms the individual models through a strategic combination of them. Three techniques of combining (Simple Averaging, Regression and Outperformance) were tested. Though the individual models showed satisfactory performance by themselves, the combination techniques were able to outperform the individual models. Regression technique of combining was seen to be the most effective of all. The predicted output power through this technique was seen to greatly fit with the measured data with an NMSE of 1.03% for the test year. The combination techniques have also demonstrated more stable performance than the individual models while tested with the extreme cases of windy and less windy weeks.