Abstract:
This paper presents methods for forecasting solar
power generation by a solar plant. Solar power generation
depends primarily on relative position of sun and some
extrinsic as well as intrinsic factors. Extrinsic factors such as
cloud cover, temperature, wind speed, rainfall and humidity
have been used with intrinsic ones such as degradation of solar
panels as inputs for proposed techniques for generation
forecasting. The authors have used multiple linear regression,
logarithmic regression, polynomial regression and artificial
neural network method on the data of past one year (January
2014-December 2014) for creation of forecasting models. These
forecasting models are then compared on the basis of their
accuracy to forecast the solar generation.
