Neural Networks Technology for Evaluating Solar Energy Resources in Tashkent

Abstract

In terms of economy, electricity is a commodity capable of being bought, sold and traded. Electricity is difficult to store, and it has to be available on demand. Consequently, unlike other products, it’s impossible, under normal operating conditions, to keep it in stock, to ration it, or to have customers queue for it. The solar energy generating system, whether grid-connected or stand-alone, is most commonly used in places when possible to install solar equipment (roofs, pollutes areas, closed rubbish dumps, rural and suburban areas). It is based on converting solar radiation (i.e., photons that are sent from the sun) to produce electricity. The PV system has a lot of ways of applications. For example, in developing countries, PV is used for basic life needs, such as heating and cooking, while in developed countries, the system is used to supply electricity for homes and grids. Due to its importance in the solar energy field, global solar radiation data (GSR) forecasting has become more popular to facilitate solar system installation. Solar radiation prediction and forecasting carry out considering global weather solar radiation data. Ambient temperature and relative humidity data are the most commonly used parameters to predict solar radiation and special techniques used in this study are artificial neural networks