Photovoltaic power forecasting using wavelet Neuro-Fuzzy for active solar trackers

Article type: Research Article

Authors: Stefenon, Stéfano Frizzo^{a; b; *} | Kasburg, Christopher^a | Freire, Roberto Zanetti^c | Silva Ferreira, Fernanda Cristina^a | Bertol, Douglas Wildgrube^b | Nied, Ademir^b

Affiliations: [a] Electrical Engineering, Center of Exact and Technological Sciences (CCET), University of Planalto Catarinense (UNIPLAC), Lages SC, Brazil | [b] Electrical Engineering Postgraduate Program (PPGEE), Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville SC, Brazil | [c] Industrial and Systems Engineering Graduate Program (PPGEPS), Polytechnic School (EP), Pontifical Catholic University of Parana (PUCPR), Curitiba PR, Brazil

Correspondence: [*] Corresponding author. Stéfano Frizzo Stefenon, E-mail: [email protected].

Abstract: 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.

Keywords: Photovoltaic panels, Neuro-Fuzzy inference system, time series forecasting, wavelets, solar trackers

DOI: 10.3233/JIFS-201279

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1083-1096, 2021