Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models
The research is set to predict solar irradiation using various machine learning algorithms. This is done in order to construct and develop a high-efficiency prediction model that uses actual meteorological data to predict daily solar irradiance for the town of Zaria, Nigeria. To assist utilities wo...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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The University of Lahore
2024-07-01
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| Series: | Pakistan Journal of Engineering & Technology |
| Subjects: | |
| Online Access: | https://journals.uol.edu.pk/pakjet/article/view/3101 |
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| _version_ | 1823857295724904448 |
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| author | Ibrahim Abdulwahab Sulaiman Haruna Sulaiman Umar Musa Ibrahim Abdullahi Shehu Abdullahi Kakumi Musa Ismaila Mahmud Mohammed Musa Abdullahi Abubakar Abdulrahman Olaniyan |
| author_facet | Ibrahim Abdulwahab Sulaiman Haruna Sulaiman Umar Musa Ibrahim Abdullahi Shehu Abdullahi Kakumi Musa Ismaila Mahmud Mohammed Musa Abdullahi Abubakar Abdulrahman Olaniyan |
| author_sort | Ibrahim Abdulwahab |
| collection | DOAJ |
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The research is set to predict solar irradiation using various machine learning algorithms. This is done in order to construct and develop a high-efficiency prediction model that uses actual meteorological data to predict daily solar irradiance for the town of Zaria, Nigeria. To assist utilities working in various solar energy generation and monitoring stations in making effective solar energy generation management system decisions. Four machine learning models (artificial neural network (ANN), decision tree (DT), random forest (RF), and gradient boost tree (GBT).) were used to predict and compare actual and anticipated solar radiation values. The results reveal that meteorological characteristics (min-humidity, max-temperature, day, month, and wind direction) are critical in machine learning model training. The solar radiation prediction skills of multi-layer perceptron and decision tree models were low. In the prediction of daily solar irradiation, the ensemble learning models of random forest and gradient boost tree outperformed the other models. The random forest model is shown to be the most accurate in predicting solar irradiation.
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| format | Article |
| id | doaj-art-9cd6b5e053c844ff8f6e961c84cc48ae |
| institution | Kabale University |
| issn | 2664-2042 2664-2050 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | The University of Lahore |
| record_format | Article |
| series | Pakistan Journal of Engineering & Technology |
| spelling | doaj-art-9cd6b5e053c844ff8f6e961c84cc48ae2025-02-11T22:25:22ZengThe University of LahorePakistan Journal of Engineering & Technology2664-20422664-20502024-07-0172Solar Irradiance Prediction for Zaria Town Using Different Machine Learning ModelsIbrahim Abdulwahab0Sulaiman Haruna Sulaiman1Umar Musa2Ibrahim Abdullahi Shehu3Abdullahi Kakumi Musa4Ismaila Mahmud5Mohammed Musa6Abdullahi Abubakar7Abdulrahman Olaniyan8Department of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, Nigeria The research is set to predict solar irradiation using various machine learning algorithms. This is done in order to construct and develop a high-efficiency prediction model that uses actual meteorological data to predict daily solar irradiance for the town of Zaria, Nigeria. To assist utilities working in various solar energy generation and monitoring stations in making effective solar energy generation management system decisions. Four machine learning models (artificial neural network (ANN), decision tree (DT), random forest (RF), and gradient boost tree (GBT).) were used to predict and compare actual and anticipated solar radiation values. The results reveal that meteorological characteristics (min-humidity, max-temperature, day, month, and wind direction) are critical in machine learning model training. The solar radiation prediction skills of multi-layer perceptron and decision tree models were low. In the prediction of daily solar irradiation, the ensemble learning models of random forest and gradient boost tree outperformed the other models. The random forest model is shown to be the most accurate in predicting solar irradiation. https://journals.uol.edu.pk/pakjet/article/view/3101Machine Learning, Renewable Energy, Solar Irradiance, Weather Prediction. |
| spellingShingle | Ibrahim Abdulwahab Sulaiman Haruna Sulaiman Umar Musa Ibrahim Abdullahi Shehu Abdullahi Kakumi Musa Ismaila Mahmud Mohammed Musa Abdullahi Abubakar Abdulrahman Olaniyan Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models Pakistan Journal of Engineering & Technology Machine Learning, Renewable Energy, Solar Irradiance, Weather Prediction. |
| title | Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models |
| title_full | Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models |
| title_fullStr | Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models |
| title_full_unstemmed | Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models |
| title_short | Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models |
| title_sort | solar irradiance prediction for zaria town using different machine learning models |
| topic | Machine Learning, Renewable Energy, Solar Irradiance, Weather Prediction. |
| url | https://journals.uol.edu.pk/pakjet/article/view/3101 |
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