Predictor analysis for electricity price forecasting by multiple linear regression

Abstract

This paper examines the multiple linear regression method on electricity price forecasting. Numerous predictors are analyzed to reduce the mean absolute percentage error. The training data includes the dates from September 2018 to September 2019 from the day-ahead electricity market in Turkey. It is proved that the lagged electricity prices such as the previous one day, one week, and lagged moving average prices play a key role in electricity price estimation. Aside from other valuable coefficients, natural gas, oil, and coal prices are tested in the forecasting model. The error rates of the fuel prices are noticeably shrunk by using multiple linear regression that generates more accurate results and crucial variables influencing hourly electricity price has determined. Different training data length is an essential part of decreasing the error proportions in the electricity price estimation. Also, it is analyzed that there is no dramatic difference regarding the error rates if it is compared to the regular regression method and dynamic regression model in the forecast of electricity prices.