Electricity Load Forecasting in Smart Grids Using Support Vector Machine

One of the key issues in the Smart Grid (SG) is accurate electric load forecasting. Energy generation and consumption have highly varying. Accurate forecasting of electric load can decrease the fluctuating behavior between energy generation and consumption. By knowing the upcoming electricity load consumption, we can control the extra energy generation. To solve this issue, we have proposed a forecasting model, which consists of a two-stage process; feature engineering and classification. Feature engineering consists of feature selection and extraction. By combining Extreme Gradient Boosting (XGBoost) and Decision Tree (DT) techniques, we have proposed a hybrid feature selector to minimize the feature redundancy. Furthermore, Recursive Feature Elimination (RFE) technique is applied for dimension reduction and improve feature selection. To forecast electric load, we have applied Support Vector Machine (SVM) set tuned with three super parameters, i.e., kernel parameter, cost penalty, and incentive loss function parameter. Electricity market data is used in our proposed model. Weekly and months ahead forecasting experiments are conducted by proposed model. Forecasting performance is assessed by using RMSE and MAPE and their values are 1.682 and 12.364. The simulation results show 98% load forecasting accuracy.