@inproceedings{f8a719996901481aadc06c964ed54d9d,
title = "A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study",
abstract = "The research presented in this paper examines and develops a machine learning-based model to predict the hourly output of a rooftop solar installation in a petrochemical industrial facility in South Africa. This research attempts to thoroughly examine the factors impacting solar energy generation and then create a reliable predictive model for solar power. This work uses machine learning to produce an accurate and dependable model to increase the effectiveness of energy management systems. It was established that solar power output and efficiency depend on various environmental factors and the year's season. The results show RMSE values of 4.30, 4.22, and 11.89 for the 1D CNN, LSTM and hybrid CNN-LSTM models, respectively.",
keywords = "CNN, LSTM, Machine learning, artificial intelligence, climate change, power prediction, solar forecasting",
author = "Mashapu, {Lehlogonolo David} and Mathaba, {Tebello N.D.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 ; Conference date: 25-07-2024 Through 27-07-2024",
year = "2024",
doi = "10.1109/ICECET61485.2024.10698648",
language = "English",
series = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024",
address = "United States",
}
