IJBTT

A Review of Machine Learning Based Approaches for Solar Irradiation Forecasting
	© 2024 by IJCTT Journal
	Volume-72 Issue-11
	Year of Publication : 2024
	Authors : Vaibhav Mani Tripathi, D. A. Mehta
	DOI :  10.14445/22312803/IJCTT-V72I11P109

How to Cite?

Vaibhav Mani Tripathi, D. A. Mehta, "A Review of Machine Learning Based Approaches for Solar Irradiation Forecasting," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 83-91, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P109

Abstract
The need to swiftly migrate towards renewable and clean energy sources such as solar power has been garnering a lot importance over the last two decades due to the energy crisis and global warming. Because of the abundance of sites with strong irradiation, investments in these technologies have surged. Nevertheless, more adaptable and dependable energy production is required due to the intermittent character of solar radiation. Traditional technology is relied upon by energy system operators to fulfill grid demands. More widespread use of renewable generators requires precise estimates of future solar irradiation trends. Recent literature has employed various methods to predict renewable energy output based on historical data. Notably, solar irradiation prediction using data-driven models has gained attention. However, machine learning models face challenges due to the significant variation in solar irradiation, including periods of zero irradiation during the night. Several machine learning and deep learning models have been employed to forecast such volatile trends in solar irradiation. These models are CSVR, LSTM, Bi-LSTM, GRU, Bi-GRU, CNN-LSTM, ADHDP-Based Neural Networks, SVM, ANN, and ANFIS. Data filtering methodologies extensively used are averaging filtering, such as mean and median filters, or filtration in the transform domain, such as the DWT. Thus, the necessity of data filtration and pre processing has also been exemplified. This paper provides a comprehensive review of existing techniques in this domain, intending to highlight the salient features of contemporary work, which would allow researchers to gauge the strengths and weaknesses of existing approaches and decide upon the appropriate data-driven models for future enhancements in the domain, which happens to be the major advantage of this research work.

Keywords
Machine Learning, Deep Learning, Solar Irradiation Forecasting, Data Pre-Processing, Data Optimization, Performance Metrics.

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