Fast and Accurate Hybrid Electric Load Forecasting with Novel Feature Engineering and Optimization Framework in Smart Grid

In this paper, a fast and accurate hybrid electric load forecasting (FA-HELF) framework is proposed. The proposed FA-HELF is an integrated framework of three modules. First, random forest and relief-F algorithms are fused together to propose a hybrid feature selection technique for the purpose to eliminate redundancy. Second, the kernelbased principle component analysis is introduced for feature extraction in order to overcome the problem of dimensionality reduction. Finally, to perform fast and accurate load forecasting heuristic based optimizer is integrated with a support vector machine (SVM) forecaster. The proposed FA-HELF framework shows significant improvements than other existing forecasting models in terms of forecast accuracy and convergence rate.