Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock

Afshin Balal; Yaser Pakzad Jafarabadi; Ayda Demir; Morris Igene; Michael Giesselmann; Stephen Bayne

doi:10.28991/ESJ-2023-07-04-02

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Afshin Balal
Afshin.balal@ttu.edu
Department of Electrical and Computer Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States
Yaser Pakzad Jafarabadi Department of Electrical and Computer Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States
Ayda Demir Department of Electrical and Computer Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States
Morris Igene Department of Civil and Construction Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States
Michael Giesselmann Department of Electrical and Computer Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States
Stephen Bayne Department of Electrical and Computer Engineering, Texas Tech Univeristy, Lubbock, Texas,, United States

Vol. 7 No. 4 (2023): August

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Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy.

Doi: 10.28991/ESJ-2023-07-04-02

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Balal, A., Pakzad Jafarabadi, Y., Demir, A., Igene, M., Giesselmann, M., & Bayne, S. (2023). Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock. Emerging Science Journal, 7(4), 1052–1062. https://doi.org/10.28991/ESJ-2023-07-04-02

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Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock

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