Version 1
: Received: 23 July 2024 / Approved: 24 July 2024 / Online: 25 July 2024 (10:19:15 CEST)
How to cite:
Kadampur, M. A. B. Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy. Preprints2024, 2024071971. https://doi.org/10.20944/preprints202407.1971.v1
Kadampur, M. A. B. Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy. Preprints 2024, 2024071971. https://doi.org/10.20944/preprints202407.1971.v1
Kadampur, M. A. B. Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy. Preprints2024, 2024071971. https://doi.org/10.20944/preprints202407.1971.v1
APA Style
Kadampur, M. A. B. (2024). Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy. Preprints. https://doi.org/10.20944/preprints202407.1971.v1
Chicago/Turabian Style
Kadampur, M. A. B. 2024 "Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy" Preprints. https://doi.org/10.20944/preprints202407.1971.v1
Abstract
Renewable energy especially insights into solar energy are crucial in the energy business cycle. In this paper, an impact study of meteorological variables on solar energy production is conducted. The paper discusses the pipeline of data analysis specific to the case study and exploits open-source machine-learning algorithms to build predictive models. RandomForestRegressor and XGBoosting regressors are used to build the predictive model. The application code is integrated into an analytic software service (Power BI) and tested on Solar energy Open Data of Saudi Arabia. The solar energy production is found to be high between 800-1000 barometric pressure locations. Global Horizontal Irradiation is found to increase when the atmospheric temperature is more than 34∘ C. The impact of relative humidity is that the produced energy is high at lower humidity points. The wind speed effect is that the produced energy is significant between 2 to 4 m/s and reaches its maximum at 2.4 m/s. The predictive models produce nearly real-time predictions with Random Forest with R²: 0.909. The paper provides Global Horizontal Irradiation estimation equations using meteorological coefficients generated by the trained regressor. The paper presents implementation methodologies and records various instances of test results. It highlights the efficacy of machine learning in predictive modeling for renewable energy applications.
Keywords
Solar Energy; Predictive Modeling; Meteorological Variables; Machine Learning; RandomForestRegressor and XGBoosting; Power BI; Global Horizontal Irradiance; Saudi Arabia.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
