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Forecasting 2018, 1(1), 26-46; https://doi.org/10.3390/forecast1010003

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

1
Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA
2
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA
3
Transmission Analytics, 2025 Guadalupe St, Suite 260, Austin, TX 78705, USA
*
Author to whom correspondence should be addressed.
Received: 18 June 2018 / Revised: 3 July 2018 / Accepted: 9 July 2018 / Published: 12 July 2018
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems)
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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. View Full-Text
Keywords: ARIMA-SVM (Support Vector Machine); ARIMA-RF (Random Forest); ARIMA-GLM (Generalized Linear Model); electricity price forecasting; Iberian market; day-ahead price ARIMA-SVM (Support Vector Machine); ARIMA-RF (Random Forest); ARIMA-GLM (Generalized Linear Model); electricity price forecasting; Iberian market; day-ahead price
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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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.

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