A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study

Lehlogonolo David Mashapu; Tebello N.D. Mathaba

doi:10.1109/ICECET61485.2024.10698648

A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study

Lehlogonolo David Mashapu
, Tebello N.D. Mathaba

Postgraduate School of Engineering Management

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350395914
DOIs	https://doi.org/10.1109/ICECET61485.2024.10698648
Publication status	Published - 2024
Event	4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 - Sydney, Australia Duration: 25 Jul 2024 → 27 Jul 2024

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

Conference

Conference	4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024
Country/Territory	Australia
City	Sydney
Period	25/07/24 → 27/07/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

CNN
LSTM
Machine learning
artificial intelligence
climate change
power prediction
solar forecasting

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Access to Document

10.1109/ICECET61485.2024.10698648

Cite this

Mashapu, L. D., & Mathaba, T. N. D. (2024). A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECET61485.2024.10698648

Mashapu, Lehlogonolo David ; Mathaba, Tebello N.D. / A Machine Learning-based Short- Term Prediction Model for a Solar Plant : A South African case study. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024).

@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",

}

Mashapu, LD & Mathaba, TND 2024, A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study. in International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024, Institute of Electrical and Electronics Engineers Inc., 4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024, Sydney, Australia, 25/07/24. https://doi.org/10.1109/ICECET61485.2024.10698648

A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study. / Mashapu, Lehlogonolo David; Mathaba, Tebello N.D.
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Machine Learning-based Short- Term Prediction Model for a Solar Plant

T2 - 4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

AU - Mashapu, Lehlogonolo David

AU - Mathaba, Tebello N.D.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - CNN

KW - LSTM

KW - Machine learning

KW - artificial intelligence

KW - climate change

KW - power prediction

KW - solar forecasting

UR - https://www.scopus.com/pages/publications/85207473528

U2 - 10.1109/ICECET61485.2024.10698648

DO - 10.1109/ICECET61485.2024.10698648

M3 - Conference contribution

AN - SCOPUS:85207473528

T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 July 2024 through 27 July 2024

ER -

Mashapu LD, Mathaba TND. A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024). doi: 10.1109/ICECET61485.2024.10698648

A Machine Learning-based Short- Term Prediction Model for a Solar Plant: A South African case study

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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