Comparison of Statistical Production Models for a Solar and a Wind Power Plant
Mathematical models to characterize and forecast the power production of photovoltaic and eolian plants are justified by the benefits of these sustainable energies, the increased usage in recent years, and the necessity to be integrated into the general energy system. In this paper, starting from tw...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/5/1115 |
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| author | Irina Meghea |
| author_facet | Irina Meghea |
| author_sort | Irina Meghea |
| collection | DOAJ |
| description | Mathematical models to characterize and forecast the power production of photovoltaic and eolian plants are justified by the benefits of these sustainable energies, the increased usage in recent years, and the necessity to be integrated into the general energy system. In this paper, starting from two collections of data representing the power production hourly measured at a solar plant and a wind farm, adequate time series methods have been used to draw appropriate statistical models for their productions. The data are smoothed in both cases using moving average and continuous time series have been obtained leading to some models in good agreement with experimental data. For the solar power plant, the developed models can predict the specific power of the next day, next week, and next month, with the most accurate being the monthly model, while for wind power only a monthly model could be validated. Using the CUSUM (cumulative sum control chart) method, the analyzed data formed stationary time series with seasonality. The similar methods used for both sets of data (from the solar plant and wind farm) were analyzed and compared. When compare with other studies which propose production models starting from different measurements involving meteorological data and/or machinery characteristics, an innovative element of this paper consists in the data set on which it is based, this being the production itself. The novelty and the importance of this research reside in the simplicity and the possibility to be reproduced for other related conditions even though every new set of data (provided from other power plants) requires further investigation. |
| first_indexed | 2024-03-11T07:19:08Z |
| format | Article |
| id | doaj.art-ee1af2a0b3034a14a1f9af5460ad6ead |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T07:19:08Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ee1af2a0b3034a14a1f9af5460ad6ead2023-11-17T08:08:26ZengMDPI AGMathematics2227-73902023-02-01115111510.3390/math11051115Comparison of Statistical Production Models for a Solar and a Wind Power PlantIrina Meghea0Department of Mathematical Methods and Models, Faculty of Applied Sciences, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, RomaniaMathematical models to characterize and forecast the power production of photovoltaic and eolian plants are justified by the benefits of these sustainable energies, the increased usage in recent years, and the necessity to be integrated into the general energy system. In this paper, starting from two collections of data representing the power production hourly measured at a solar plant and a wind farm, adequate time series methods have been used to draw appropriate statistical models for their productions. The data are smoothed in both cases using moving average and continuous time series have been obtained leading to some models in good agreement with experimental data. For the solar power plant, the developed models can predict the specific power of the next day, next week, and next month, with the most accurate being the monthly model, while for wind power only a monthly model could be validated. Using the CUSUM (cumulative sum control chart) method, the analyzed data formed stationary time series with seasonality. The similar methods used for both sets of data (from the solar plant and wind farm) were analyzed and compared. When compare with other studies which propose production models starting from different measurements involving meteorological data and/or machinery characteristics, an innovative element of this paper consists in the data set on which it is based, this being the production itself. The novelty and the importance of this research reside in the simplicity and the possibility to be reproduced for other related conditions even though every new set of data (provided from other power plants) requires further investigation.https://www.mdpi.com/2227-7390/11/5/1115time seriesmoving averagestatistical modelingstatistical methodsproduction forecastingsolar power plant |
| spellingShingle | Irina Meghea Comparison of Statistical Production Models for a Solar and a Wind Power Plant Mathematics time series moving average statistical modeling statistical methods production forecasting solar power plant |
| title | Comparison of Statistical Production Models for a Solar and a Wind Power Plant |
| title_full | Comparison of Statistical Production Models for a Solar and a Wind Power Plant |
| title_fullStr | Comparison of Statistical Production Models for a Solar and a Wind Power Plant |
| title_full_unstemmed | Comparison of Statistical Production Models for a Solar and a Wind Power Plant |
| title_short | Comparison of Statistical Production Models for a Solar and a Wind Power Plant |
| title_sort | comparison of statistical production models for a solar and a wind power plant |
| topic | time series moving average statistical modeling statistical methods production forecasting solar power plant |
| url | https://www.mdpi.com/2227-7390/11/5/1115 |
| work_keys_str_mv | AT irinameghea comparisonofstatisticalproductionmodelsforasolarandawindpowerplant |