Time Series Prediction of Renewable Energy: What We Can and What We Should Do Next

Abstract

We summarize our recent developments of time series prediction for renewable energy. Given the past parts of high-dimensional time series for renewable energy outputs, we can predict their multistep future in real time with confidence intervals. We also proposed a way to evaluate the closeness in the high-dimensional space for improving the prediction, and an index showing when the prediction is more likely to fail. In addition, it is straightforward to apply the proposed framework to predict the electricity demands. Therefore, we can generate information necessary to consider efficient unit commitments for a case where more renewable energy resources are installed.