Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

Giovanni Masala () and Amelie Schischke
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Giovanni Masala: Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, Italy
Amelie Schischke: Institute of Materials Resource Management, University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Bavaria, Germany

Econometrics, 2024, vol. 12, issue 4, 1-15

Abstract: Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

Keywords: renewable energy; electricity price; income; time series forecasting; machine learning techniques; backtesting (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2024
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