NDLI: Hybrid deep learning for power generation forecasting in active solar trackers

Content Provider	IET Digital Library
Author	Stefenon, Stéfano Frizzo Kasburg, Christopher Nied, Ademir Klaar, Anne Carolina Rodrigues Ferreira, Fernanda Cristina Silva Branco, Nathielle Waldrigues
Abstract	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.
Starting Page	5667
Ending Page	5674
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 23, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0814
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-09-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Active Solar Trackers Digital Signal Processing Dual-axis Solar Trackers Electricity Demand Feature Extraction Filtering Method in Signal Processing Filtering Theory Hybrid Deep Learning Integral Transforms Learning in AI Load Forecasting Long–short-term Memory Neural Computing Technique Nonlinear Data Photovoltaic Panel Generation Photovoltaic Power Forecasting Photovoltaic Power System Power Engineering Computing Power Generation Forecasting Power System Planning And Layout Recurrent Neural Nets Signal Characteristic Signal Denoising Signal Filtering Signal Noise Reduction Solar Power Stations Solar Uptake Wavelet Energy Coefficient Wavelet LSTM Wavelet Neural Nets Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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