Short Term Load and Price Forecasting using Tuned Parameters for K-Nearest Neighbors

Short term electricity load and price accurate forecasting are the key areas that need to be addressed in Smart Grids (SG). In this paper, a new model is proposed for accurate short term forecasting of load and price. First, irrelevant features are removed using Recursive Feature Elimination (RFE) and Decision Tree Regressor (DTR). Then, further dimensionality reduction is done by removing redundancy using Singular Value Decomposition (SVD) for feature extraction. Finally, remaining features are given to Enhanced K-Nearest Neighbor (EKNN) for forecasting of both load and price. The proposed EKNN achieved better results than Support Vector Regressor (SVR) and normal K-Nearest Neighbors (KNN). EKNN achieved 12% more accuracy in terms of forecasting than normal KNN and 63% more than SVR.