Abstract
This paper proposes an ANN-based framework for forecasting the daily solar irradiance with the meteorological data collected over 10 years for Hisar city, located in Haryana. Meteorological information like temperature, relative humidity, and average wind speed is used as input variables for estimating sun irradiation in order to obtain high accuracy under various weather situations. Meteorological information is used as input variables for estimating the solar irradiation in order to obtain acceptable accuracy in a variety of weather circumstances. In addition, it was suggested in this paper that the Artificial Neural Network (ANN) model be trained using the Harris Hawks Optimization (HHO) Algorithm. The following task is to choose the best solar panel (SP) out of the thousands that are currently on the market. Due to a crucial component like weather, choosing the finest SP is important. Hence, this paper introduces a score-based model for selecting the best panel by considering the different criteria of the eleven SPs that are used as test panels. The annual power produced by panel P11 is 79.67% better than panel P1. Finally, based on the average power generated, the proposed panel P11 is regarded as the best panel when compared to other panels.
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