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Towards broad, low-cost solar radiation forecasting using machine learning

Chantampakis Nikolaos

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Summary

In the past few years, solar radiation prediction has been paramount in a multitude of sectors, from energy production via renewable energy sources, to tracking climate change, among others. So far, work in the area lacks in large area coverage, ease of access, or uses past solar radiation readings, relying on related equipment being already on-site. In this work, we provide insight into the efficacy of neural networks in the area, accompanied with data sourced from varying providers. In order to achieve this, we create and vet a weather reading dataset from a large variety of stations, which are more indicative of what smaller organizations or individuals may have access to, instead of more tailored datasets. We utilize this dataset to train a number of neural networks, each with different architectures, and evaluate their results so as to set a standard to be improved upon in later work utilizing a similar type of dataset. The results indicate that, even utilizing a much broader dataset than what has been used in the past, neural networks show promise in this area, especially with more targeted implementations.

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