Version 1
: Received: 16 August 2023 / Approved: 16 August 2023 / Online: 18 August 2023 (09:26:27 CEST)
How to cite:
Chirchir, I.R.; Park, S.J.; Kommen, G. Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya. Preprints2023, 2023081321. https://doi.org/10.20944/preprints202308.1321.v1
Chirchir, I.R.; Park, S.J.; Kommen, G. Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya. Preprints 2023, 2023081321. https://doi.org/10.20944/preprints202308.1321.v1
Chirchir, I.R.; Park, S.J.; Kommen, G. Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya. Preprints2023, 2023081321. https://doi.org/10.20944/preprints202308.1321.v1
APA Style
Chirchir, I.R., Park, S.J., & Kommen, G. (2023). Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya. Preprints. https://doi.org/10.20944/preprints202308.1321.v1
Chicago/Turabian Style
Chirchir, I.R., Soo jin Park and George Kommen. 2023 "Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya" Preprints. https://doi.org/10.20944/preprints202308.1321.v1
Abstract
This study focused on the predictive models incorporating machine learning techniques that induce new dynamics for forecasting energy generation, enabling effective planning, financing, and system monitoring. The research developed a machine learning-based power generation prediction model tailored explicitly for Kenya's Garissa solar power plant. The selected model demonstrated a root mean squared error of 5.23 during evaluation, resulting in a prediction accuracy of 90.42%. This high accuracy indicates that the model can be relied upon for precise generation prediction, facilitating effective planning, and system performance monitoring
Keywords
Energy Forecasting; Modeling; Electricity Mix; Machine Learning Algorithms
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
