Open AccessHybrid deep learning for power generation forecasting in active solar trackers
Author(s) -
Frizzo Stefe Stéfano,
Kasburg Christopher,
Nied Ademir,
Rodrigues Klaar Anne Carolina,
Silva Ferreira Fernanda Cristina,
Waldrigues Branco Nathielle
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0814
Subject(s) - bittorrent tracker , computer science , photovoltaic system , wavelet , solar tracker , artificial intelligence , solar power , solar energy , filter (signal processing) , power (physics) , engineering , computer vision , physics , electrical engineering , quantum mechanics , eye tracking
To meet the growing electricity demand for consumers, it is necessary to use more efficient systems. The solar trackers stand out among the applications that can improve the efficiency of photovoltaic panel generation by increasing their solar uptake. For solar trackers to be more efficient, they can base their position update on a generation forecast and thus perform the control action only when there is greater efficiency in this update. For generation forecast, the long–short‐term memory (LSTM) can handle a large volume of non‐linear data. Furthermore, to improve the analysis, it is possible to apply signal filtering techniques. The wavelet energy coefficient is a technique used to reduce signal noise and extract features; this technique performs the filter and preserves the signal characteristic. In this study, the authors present a combination of wavelet energy coefficient and LSTM, defined as wavelet LSTM, to perform photovoltaic power forecasting in the dual‐axis solar trackers.