Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Abstract

Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost. However, most of the current research only focuses on one-step STLF, which cannot satisfy the human-beings needs. Besides, short-term consumption fluctuates significantly in different periods and people, which increases the difficulty of forecasting. In this paper, we present a novel deep model named multi-channel long short-term memory (LSTM) with time location (TL-MCLSTM) in a multiple output strategy to forecast the multi-step short-term power consumption. The proposed model contains three channels: power consumption, time location, and customer behavior channels, respectively. Power consumption channel reflects the change and general trend of use; Time location channel reflects the hidden pattern of customer habits, which records the information consisting of time, day of the week, holidays. Moreover, we combine a convolution autoencoder and k-means to identify the type of behavior at the customer behavior channel. Power consumption and time location channels are trained individually through the LSTM as it has excellent memory function. Extracted features from LSTM in power consumption and time location channels are combined with customer behavior as comprehensive features to forecast. We designed, trained, and verified our proposed deep model on two nature data sets, and compared with other leading deep learning-based methods. The comparative studies have confirmed the effectiveness and priority of TL-MCLSTM for multi-step short-term consumption forecasting.

Keywords

• Short-term load forecasting (STLF)
• smart grid
• LSTM
• multi-step forecasting
• convolution autoencoder