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Solar Energy Prediction using Least Square Linear Regression Method

Author : Suruchi Dedgaonkar, Vishal Patil, Niraj Rathod, Gajanan Hakare & Jyotiba Bhosale

Pages : 1549-1552
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A challenge with renewable energy prediction is that their power generation is intermittent and uncontrollable. But, prediction of renewable energy is important, because of variation in weather parameters and demand of energy at each location. The solar energy is an infinitely available source of energy. The amount of solar radiation varies at every location depending on the weather factors like temperature, rainfall, humidity, wind speed, etc. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes is a challenging problem. To address the problem, in this paper, we creating prediction models for solar power generation from National Data Centre (NDC) weather forecasts data using machine learning techniques.

 Keywords: Least square linear regression, solar energy predictions, machine learning.

Article published in International Journal of Current Engineering and Technology, Vol.6, No.5 (Oct-2016)

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