An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid

Abstract

The significant part of smart grid is to make smart grid cost-efficient by predicting electricity price and load. To improve the prediction performance we proposed an Efficient Convolutional Neural Network (ECNN) and Efficient K-nearest Neighbour (EKNN) in which the parameters are tuned. It may be difficult to deal with huge amount of load data that is coming from the electricity market. To overcome this issue, we incorporated three modules in the proposed methodology. The proposed model consists of feature engineering and classification. Feature engineering is a two-step process (feature selection and feature extraction); for the purpose of feature selection Mutual Information (MI) is used which reduces the redundancy among features and for feature extraction Recursive Feature Elimination (RFE) is used to extract the principle features from the selected features and reduces the dimensionality of features. Finally, after training the data-set and the removal of the duplicate features load prediction is done by ECNN and EKNN. The ECNN and EKNN outperforms better then traditional Convolutional Neural Network (CNN) and K-nearest Neighbour (KNN). The forecast performance is evaluated by comparing the results with MAPE, RMSE, MAE and MSE. i.e. 10.8, 7.5, 7.15, and 10.4 respectively.