Please use this identifier to cite or link to this item:
https://dspace.chmnu.edu.ua/jspui/handle/123456789/1440
Title: | Hyperparameter Optimization of LSTM MPPT Controller for Solar Power Plants |
Authors: | Nechakhin, V. Kalinina, I. Gozhyj, A. |
Keywords: | Hyperparameter Optimization Long Short-Term Memory (LSTM) Neural Networks Maximum Power Point Tracking Renewable Energy Controller Solar Power Plants |
Issue Date: | 2023 |
Publisher: | IEEE |
Abstract: | Efficient tracking of the maximum power point (MPP) is crucial for optimizing energy extraction in solar power plants. This paper focuses on hyperparameter optimization of Long Short-Term Memory (LSTM) neural networks for MPP tracking control. Traditional and emerging MPP tracking techniques are reviewed, and the potential of LSTM-based controllers is explored. The study rigorously investigates the impact of hyperparameters such as learning rate, layers, hidden units, and dropout rate on the controller's efficiency. By navigating the hyperparameter space, the research aims to uncover optimal configurations that enhance MPP tracking precision and performance in solar power plants. |
Description: | Nechakhin, V., Kalinina, I., & Gozhyj, A. (2023). Hyperparameter Optimization of LSTM MPPT Controller for Solar Power Plants. International Scientific and Technical Conference on Computer Sciences and Information Technologies. IEEE. Lviv. doi: 10.1109/CSIT61576.2023.10324254 |
URI: | record.uri https://ieeexplore.ieee.org/document/10324254 https://dspace.chmnu.edu.ua/jspui/handle/123456789/1440 |
ISBN: | 979-835036046-2 |
ISSN: | 27663655 |
Appears in Collections: | Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Nechakhin, V., Kalinina, I., Gozhyj, A..pdf | 59.65 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.