An Optimized Linear-Kernel Support Vector Machine for Electricity Load and Price Forecasting in Smart Grids

Abstract

In smart grids, one of the key issues is accurate forecasting of electricity load and price to reduce the gap between generation and consumption of electricity. To address this issue, a framework has been proposed, in which feature selection has been done by Random Forest (RF) technique in both datasets of load and price. For prediction, RF, Support Vector Machine (SVM) and SVM along with an enhanced linear kernel and tuned parameters are used. New York electricity market data for load (MWh) and price ($) has been taken for this purpose. Daily and weekly forecasting results have been taken by the proposed system. Several performance evaluation techniques have been used to evaluate prediction results. The results show that our proposed technique performed better (0.07% for load and 0.12% for price) than default linear-kernel SVR.