Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models
Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2021/6777488 |
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| author | Laurentiu Fara Alexandru Diaconu Dan Craciunescu Silvian Fara |
| author_facet | Laurentiu Fara Alexandru Diaconu Dan Craciunescu Silvian Fara |
| author_sort | Laurentiu Fara |
| collection | DOAJ |
| description | Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production. |
| format | Article |
| id | doaj-art-82bee6c1fd824a4d91fe15d866938c42 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-82bee6c1fd824a4d91fe15d866938c422025-02-03T07:23:26ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2021-01-01202110.1155/2021/67774886777488Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced ModelsLaurentiu Fara0Alexandru Diaconu1Dan Craciunescu2Silvian Fara3Department of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042, RomaniaDepartment of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042, RomaniaDepartment of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042, RomaniaDepartment of Physics, Faculty of Applied Sciences, Polytechnic University of Bucharest, 060042, RomaniaAccurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.http://dx.doi.org/10.1155/2021/6777488 |
| spellingShingle | Laurentiu Fara Alexandru Diaconu Dan Craciunescu Silvian Fara Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models International Journal of Photoenergy |
| title | Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models |
| title_full | Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models |
| title_fullStr | Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models |
| title_full_unstemmed | Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models |
| title_short | Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models |
| title_sort | forecasting of energy production for photovoltaic systems based on arima and ann advanced models |
| url | http://dx.doi.org/10.1155/2021/6777488 |
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