Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
---|---|---|
Article Number | 10025 | |
Number of page(s) | 16 | |
Section | Grid Connected Systems | |
DOI | https://doi.org/10.1051/e3sconf/202454010025 | |
Published online | 21 June 2024 |
Solar Energy Forecast for Integration of Grid and Balancing Power Using Profound Learning
Assistant Professor, Department of CS & IT, Kalinga University, Naya Raipur, Chhattisgarh, India .
* ku.kumarshwetabh@kalingauniversity.ac.in
** ku.nikitapathrotkar@kalingauniversity.ac.in
The rapid and unexpected advancements in solar photovoltaic (PV) technology pose a future challenge for power sector experts responsible for managing the distribution of electricity, given the technology’s direct reliance on atmospheric and weather conditions. Therefore, the development of reliable predictive models for short-term solar PV generation forecasts becomes critically important to maintain a stable power supply and ensure seamless grid operations. With the evolution of deep learning and its intricate models, its application in this domain offers a more efficient means of achieving precise forecasts. As a result, the proposed system undergoes the following stages: a) Collecting data from the Sky Images and Photovoltaic Power Generation Dataset (SKIPDD) hosted on a GitHub repository, which contains one-minute intervals of 64x64 sky images and concurrent PV power generation data. b) Enhancing the PV input data through processes such as geometric correction, ortho rectification, pan sharpening, block adjustment, and histogram equalization. c) Extracting PV-related features from these images using an Autoencoder. d) forecasting using integration of CNNbased Bi-LSTM. Experimental evaluation states that the proposed system (ACNN-BiLSTM) outperforms better on various measures (accuracy:0.95, MSE:0.08, MAE: 0.02).
Key words: Bi-LSTM / CNN / Deep learning / Forecast model / Grid / Photovoltaic Power / Solar Power
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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