Time-Shifted Gramian Angular Field and Recursive Plot Convolutional Neural Network to Adapt Solar Cell Dataset Overfitting in Hybrid Power Generation

Abstract

This Solar cell power generation has been employed widely. Battery storage becomes an important part of ensuring output availability. However, battery prices and technologies are costly. Hybrid generation for daylight supply becomes one of the solutions. This paper proposed hybrid solar cell-diesel power generation. Diesel power generation should maintain overall power requirements to fulfill demand. Since irradiation changes relative to the sun's position on the earth, solar cell power output varies overtime. As the solar cell output changes overtime, the diesel power generation should be determined. The differences between the predicted solar power and the energy demand determine the energy amount that the diesel generator should provide. In order to provide prediction, convolutional neural networks utilizing multilayer perceptron with hyper-parameter optimizations and statistics transformation, as well as data transformation, are employed. However, solar cell data is highly overfitted. The folded and time-shifted gramian angular field and recursive plot are then proposed. As a result, the proposed methods can reduce overfitting on solar cell datasets to increase the prediction performances.