Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices play a crucial role. The more accurate the prediction is, the lower the market risk is. In this paper, several machine learning algorithms (Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including Mean Absolute Error (MAE), R-index, Mean Absolute Percentage Error (MAPE), and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and Tree-based models outperform other models at different time horizons.

Forecasting electricity price in different time horizons: an application to the Italian electricity market / Hosseiniimani, Seyedmahmood; Bompard, Ettore; Colella, Pietro; Huang, Tao. - In: IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS. - ISSN 0093-9994. - ELETTRONICO. - 57:6(2021), pp. 5726-5736. [10.1109/TIA.2021.3114129]

Forecasting electricity price in different time horizons: an application to the Italian electricity market

HOSSEINIIMANI SEYEDMAHMOOD;Bompard Ettore;Colella Pietro;Huang Tao
2021

Abstract

Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices play a crucial role. The more accurate the prediction is, the lower the market risk is. In this paper, several machine learning algorithms (Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including Mean Absolute Error (MAE), R-index, Mean Absolute Percentage Error (MAPE), and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and Tree-based models outperform other models at different time horizons.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2927539