A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour () and Mike Quashie
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Daniel Manfre Jaimes: Arcus Power, Calgary, AB T2P 3C5, Canada
Manuel Zamudio López: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Hamidreza Zareipour: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Mike Quashie: Arcus Power, Calgary, AB T2P 3C5, Canada

Forecasting, 2023, vol. 5, issue 3, 1-23

Abstract: This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting (XGBoost) models are stacked up to produce supplementary price forecasts. The final forecasts are then picked depending on how the predictions compare to a price spike threshold. On the “horizontal” dimension, five models are designed to extend the forecasting horizon to four days. This is an important requirement to make forecasts useful for market participants who trade energy and ancillary services multiple days ahead. The horizontally cascaded models take advantage of the availability of specific public data for each forecasting horizon. To enhance the forecasting capability of the model in dealing with price spikes, we deploy a previously unexplored input in the proposed methodology. That is, to use the recent variations in the output power of thermal units as an indicator of unplanned outages or shift in the supply stack. The proposed method is tested using data from Alberta’s electricity market, which is known for its volatility and price spikes. An economic application of the developed forecasting model is also carried out to demonstrate how several market players in the Alberta electricity market can benefit from the proposed multi-day ahead price forecasting model. The numerical results demonstrate that the proposed methodology is effective in enhancing forecasting accuracy and price spike detection.

Keywords: electricity price forecasting; electricity price spikes; long short term memory neural network; extreme gradient boosting (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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