Short Term Solar Irradiation Forecasting using CEEMDAN Decomposition Based BiLSTM Model Optimized by Genetic Algorithm Approach

Abstract

An accurate short-term solar irradiation forecasting is requiredregarding smart grid stability and to conduct bilateral contract negotiations between suppliers and customers. Traditional machine learning models are unable to acquire and to rectify nonlinear properties from solar datasets, which not only complicate model formation but also lower prediction accuracy. The present research paper develops a deep learningbased architecture with a predictive analytic technique to address these difficulties. Using a sophisticated signal decomposition technique, the original solar irradiation sequences are decomposed into multiple intrinsic mode functions to build a prospective feature set. Then, using an iteration strategy, a potential range of frequency associated to the deep learning model is generated. This method is developed utilizing a linked algorithm and a deep learning network. In comparison with conventional models, the suggested model utilizes sequences generated through preprocessing methods, significantly improving prediction accuracywhen confronted with a high resolution dataset created from a big dataset.On the other hand, the chosen dataset not only performs a massive data reduction, but also improves forecasting accuracy by up to 20.74 percent across a range of evaluation measures. The proposed model achieves lowest annual average RMSE (1.45W/m2), MAPE (2.23%) and MAE (1.34W/m2) among the other developed models for 1-hr ahead solar GHI, respectively, whereas forecast-skill obtained by the proposed model is 59% with respect to benchmark model. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.