Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting
The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltai...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/19/6075 |
| _version_ | 1797516666857324544 |
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| author | Guilherme Fonseca Bassous Rodrigo Flora Calili Carlos Hall Barbosa |
| author_facet | Guilherme Fonseca Bassous Rodrigo Flora Calili Carlos Hall Barbosa |
| author_sort | Guilherme Fonseca Bassous |
| collection | DOAJ |
| description | The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R<sup>2</sup> greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead. |
| first_indexed | 2024-03-10T07:04:05Z |
| format | Article |
| id | doaj.art-abb4c99a2d3646958d0ec658eaa2540e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:04:05Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-abb4c99a2d3646958d0ec658eaa2540e2023-11-22T15:58:55ZengMDPI AGEnergies1996-10732021-09-011419607510.3390/en14196075Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power ForecastingGuilherme Fonseca Bassous0Rodrigo Flora Calili1Carlos Hall Barbosa2Graduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, BrazilGraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, BrazilGraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro—PUC-Rio, Rio de Janeiro 22451-900, BrazilThe rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R<sup>2</sup> greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.https://www.mdpi.com/1996-1073/14/19/6075solar energyneural networkssky-cameraforecastingrenewable energyenergy quality |
| spellingShingle | Guilherme Fonseca Bassous Rodrigo Flora Calili Carlos Hall Barbosa Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting Energies solar energy neural networks sky-camera forecasting renewable energy energy quality |
| title | Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting |
| title_full | Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting |
| title_fullStr | Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting |
| title_full_unstemmed | Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting |
| title_short | Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting |
| title_sort | development of a low cost data acquisition system for very short term photovoltaic power forecasting |
| topic | solar energy neural networks sky-camera forecasting renewable energy energy quality |
| url | https://www.mdpi.com/1996-1073/14/19/6075 |
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