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Published:
Apr 12, 2024DOI:
10.4314/njt.v43i1.15Keywords:
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Main Article Content
Optimization and modeling of solar energy with artificial neural networks
Abstract
Solar energy represents one of the emerging frontiers in renewable energy, offering significant potential to address the issues of energy unavailability and instability in Uyo (Nigeria). A crucial step in overcoming these challenges is accurately predicting the amount of solar energy that can be harnessed at a specific location. This research focused on achieving optimal solar power prediction, with the following objectives; identifying and investigating the mathematical relationships between relevant variables and parameters. To ensure precise predictions, artificial neural networks (ANN) were employed, utilizing both forward and backward propagation techniques. The input data for the ANN comprised radiation data obtained from a secondary source, the solar panel's size or area from the manufacturer, the panel's efficiency, and its performance ratio – all of which determined the electricity produced in kilowatts. The ANN was trained and tested using meteorological data, enabling accurate predictions of optimal electricity generation for the location. Notably, the hourly predictions reached their peak by 1 PM at the geographic location (5.2N and 7.5E), indicating that the highest levels of solar power were attainable during this daily period. Moreover, the pattern of monthly average solar power exhibited optimal predictions in January. Influenced by meteorological factors, a significant rise and fall in August, commonly referred to as the 'August Break’ featured. The results demonstrated exceptional accuracy with minimal error margins (mean absolute error (MAE) of 0.03, mean squared error (MSE) of 0.0, and root mean squared error (RMSE) of 0.03). This high level of accuracy rendered the predictions reliable, making them suitable for consultancy services. Additionally, the potential for future work and expansion was evident, as the ANN could incorporate five or more years of radiation data for further improvements and insights.
