Daily Electricity Consumption Forecasting Based on Lazy Learning

Abstract

Daily electricity consumption is varying randomly. To improve forecasting accuracy, a Lazy Learning (LL) model is proposed. LL aims to build the regression forecasting models upon vectors which are chosen by K-vector nearest neighbors (K-VNN) method. K-VNN can solve overfitting problem and high accuracy can be ensured. Since there are many factors related to electricity consumption, Grey T's correlation degree is used to determine key indexes to further improve the running efficiency of the model. In addition, fuzzy C-means (FCM) clustering is applied to explore the similar scenarios, then the searching scope of LL is reduced. A case studied in one building in Shanghai shows the proposed method can enhance the accuracy and efficiency of electricity consumption forecasting.