Hybrid deep learning for power generation forecasting in active solar trackers
Hybrid deep learning for power generation forecasting in active solar trackers
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DOI：
10.1049/iet-gtd.2020.0814
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发表时间：
2020-12-04
影响因子：
2.5
通讯作者：
Waldrigues Branco, Nathielle
中科院分区：
文献类型：
--
作者：
Frizzo Stefenon, Stefano;Kasburg, Christopher;Waldrigues Branco, Nathielle
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.
