AN EFFICIENT ENERGY PREDICTION MODEL FOR SOLAR ENERGY POWER SYSTEM USING AI TECHNIQUE

Abstract

Science is looking for ways to slow down worldwide warming and lessen the effects of long-term fossil fuel use in the production of electricity. In this regard, one of the best answers is to deploy and promote renewable energy in many ways. A significant obstacle faces to run power plants through the economic dispatch function of connected electric networks. Analysis of Advancement in solar power generation is crucial for increasing the effectiveness of economic dispatch, reducing reliance on fossil fuels, and supporting energy management systems. To forecasting time series solar energy based on upcoming meteorological conditions, two complementing models are proposed in this work. With offline training, a model based on linear regression (LR) and a knowledge-based neural network (KBNN) is used to forecast solar power. Under the guidance of the specified input parameter selection approach, LR is constructed after there is sufficient training data. KBNN is utilized to make use of the existing prediction models when there is a lack of training data. A KBNN model can be a useful technique to increase the predicted accuracy produced by any models used for brief training data, according to test findings using real data sets. An LR model can deal effectively with linear data, but a KBNN model can cope effectively with nonlinear behaviour. Additionally, the results demonstrate the effectiveness of LR shows correlation coefficient (R2) is 98% with root mean square error 45 and KBNN shows correlation coefficient (R2) is 99% with root mean square error 44 in providing a reliable version, particularly when a small training set is available. The results further demonstrate that KBNN can create a reliable model even there is not enough data for training.