A Function-on-Function Linear Regression Approach for Short-Term Electric Load Forecasting

As more and more renewable energy options have been added to the electrical grid, the need for a more efficient, robust and smarter grid has increased. The number of electric vehicles would also increase in the future which would result in a significant amount of strain on the electrical grid. Therefore, there is an increased need for advanced short term load forecasting techniques in order to maintain the quality of the current electrical grid and ensure that all the generation resources available are utilized efficiently. In this paper, a function-on-function linear regression approach has been used to forecast short term electrical load one day in advance. Functional approach is useful as it gives a complete demand curve which makes planning easier for a utility. The forecast was obtained by using a functional B-spline approximation of past values. The performance of this functional data technique has been assessed by using historical hourly load data from the Pennsylvania, New Jersey and Maryland (PJM) electricity market. The results were obtained for four different regions separately and then aggregated. The aggregated approach is more useful as compared to overall prediction as individual models can capture details unique to a particular region. The aggregated result was compared with the overall result of whole region and an ARIMA model.