Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques
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 p...
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MDPI AG
2024-11-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/12/4/34 |
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| author | Giovanni Masala Amelie Schischke |
| author_facet | Giovanni Masala Amelie Schischke |
| author_sort | Giovanni Masala |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a2afd45bf0a347c38b83716fcdfe5415 |
| institution | DOAJ |
| issn | 2225-1146 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj-art-a2afd45bf0a347c38b83716fcdfe54152025-08-20T02:55:56ZengMDPI AGEconometrics2225-11462024-11-011243410.3390/econometrics12040034Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML TechniquesGiovanni Masala0Amelie Schischke1Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, ItalyInstitute of Materials Resource Management, University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Bavaria, GermanyHybrid 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.https://www.mdpi.com/2225-1146/12/4/34renewable energyelectricity priceincometime series forecastingmachine learning techniquesbacktesting |
| spellingShingle | Giovanni Masala Amelie Schischke Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques Econometrics renewable energy electricity price income time series forecasting machine learning techniques backtesting |
| title | Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques |
| title_full | Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques |
| title_fullStr | Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques |
| title_full_unstemmed | Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques |
| title_short | Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques |
| title_sort | forecasting wind photovoltaic energy production and income with traditional and ml techniques |
| topic | renewable energy electricity price income time series forecasting machine learning techniques backtesting |
| url | https://www.mdpi.com/2225-1146/12/4/34 |
| work_keys_str_mv | AT giovannimasala forecastingwindphotovoltaicenergyproductionandincomewithtraditionalandmltechniques AT amelieschischke forecastingwindphotovoltaicenergyproductionandincomewithtraditionalandmltechniques |