A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets
AbstractForecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods. View Full-Text
Share & Cite This Article
Angamuthu Chinnathambi, R.; Mukherjee, A.; Campion, M.; Salehfar, H.; Hansen, T.M.; Lin, J.; Ranganathan, P. A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets. Forecasting 2018, 1, 26-46.
Angamuthu Chinnathambi R, Mukherjee A, Campion M, Salehfar H, Hansen TM, Lin J, Ranganathan P. A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets. Forecasting. 2018; 1(1):26-46.Chicago/Turabian Style
Angamuthu Chinnathambi, Radhakrishnan; Mukherjee, Anupam; Campion, Mitch; Salehfar, Hossein; Hansen, Timothy M.; Lin, Jeremy; Ranganathan, Prakash. 2018. "A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets." Forecasting 1, no. 1: 26-46.