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 byK-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.
Highlights
Accurate forecasting for daily electricity consumption is of great significance to power network planning and user’s management decision
The literatures above provided important references for load forecasting, but the following issues need to be further researched: 1) The unified parameter models are adopted in present electricity consumption forecasting
The results demonstrate that the proposed method can enhance the accuracy and efficiency of electricity consumption forecasting
Summary
Accurate forecasting for daily electricity consumption is of great significance to power network planning and user’s management decision. Time series methods are adopted to identify the future electricity consumption based on historical data [1,2,3]. The forecasting accuracy can only be improved by selecting similar electricity consumption scenarios. For this purpose, a Lazy Learning (LL) method [8, 9] is proposed to forecast electricity consumption. Similar scenarios are chosen by K-vector nearest neighbors (K-VNN), so overfitting problem is overcome and high accuracy can be ensured. The results demonstrate that the proposed method can enhance the accuracy and efficiency of electricity consumption forecasting. K-vector nearest neighbors (K-VNN) method is employed to choose similar input data from database for forecasting day f. To enhance forecasting accuracy, the proportion of K in historical database samples is adjusted to 10%
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