A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Radhakrishnan Angamuthu Chinnathambi, Anupam Mukherjee, Mitch Campion, Hossein Salehfar, Timothy M. Hansen, Jeremy Lin and Prakash Ranganathan
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Radhakrishnan Angamuthu Chinnathambi: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA
Anupam Mukherjee: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA
Mitch Campion: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA
Hossein Salehfar: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA
Timothy M. Hansen: Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA
Jeremy Lin: Transmission Analytics, 2025 Guadalupe St, Suite 260, Austin, TX 78705, USA
Prakash Ranganathan: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA

Forecasting, 2018, vol. 1, issue 1, 1-21

Abstract: Forecasting 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.

Keywords: ARIMA-SVM (Support Vector Machine); ARIMA-RF (Random Forest); ARIMA-GLM (Generalized Linear Model); electricity price forecasting; Iberian market; day-ahead price (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2018
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