Photovoltaic power forecasting using wavelet Neuro-Fuzzy for active solar trackers
| Autor: | Douglas Wildgrube Bertol, Christopher Kasburg, Stéfano Frizzo Stefenon, Ademir Nied, Roberto Zanetti Freire, Fernanda Cristina Silva Ferreira |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Neuro-Fuzzy inference system Photovoltaic panels Solar trackers Time series forecasting Wavelets Neuro-fuzzy BitTorrent tracker Computer science 020209 energy Photovoltaic system General Engineering 02 engineering and technology Power (physics) Wavelet Artificial Intelligence Active solar 0202 electrical engineering electronic engineering information engineering Electronic engineering 020201 artificial intelligence & image processing |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 40:1083-1096 |
| ISSN: | 1875-8967 1064-1246 |
| Popis: | The generation of electric energy by photovoltaic (PV) panels depends on many parameters, one of them is the sun’s angle of incidence. By using solar active trackers, it is possible to maximize generation capacity through real-time positioning. However, if the engines that update the position of the panels use more energy than the difference in efficiency, the solar tracker system becomes ineffective. In this way, a time series forecasting method can be assumed to determine the generation capacity in a pre-established horizon prediction to evaluate if a position update would provide efficient results. Among a wide range of algorithms that can be used in forecasting, this work considered a Neuro-Fuzzy Inference System due to its combined advantages such as smoothness property from Fuzzy systems and adaptability property from neural networks structures. Focusing on time series forecasting, this article presents a model and evaluates the solar prediction capacity using the Wavelet Neuro-Fuzzy algorithm, where Wavelets were included in the model for feature extraction. In this sense, this paper aims to evaluate whether it is possible to obtain reasonable accuracy using a hybrid model for electric power generation forecasting considering solar trackers. The main contributions of this work are related to the efficiency improvement of PV panels. By assuming a hybrid computational model, it is possible to make a forecast and determine if the use of solar tracking is interesting during certain periods. Finally, the proposed model showed promising results when compared to traditional Nonlinear autoregressive model structures. |
| Databáze: | OpenAIRE |
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