TESLA: Taylor expanded solar analog forecasting

Abstract

With the increasing penetration of renewable energy resources within the Smart Grid, solar forecasting has become an important problem for hour-ahead and day-ahead planning. Within this work, we analyze the Analog Forecast method family, which uses past observations to improve the forecast product. We first show that the frequently used euclidean distance metric has drawbacks and leads to poor performance relatively. In this paper, we introduce a new method, TESLA forecasting, which is very fast and light, and we show through case studies that we can beat the persistence method, a state of the art comparison method, by up-to 50% in terms of root mean square error to give an accurate forecasting result. An extension is also provided to improve the forecast accuracy by decreasing the forecast horizon.