Household Electricity Load Forecasting Based on Multitask Convolutional Neural Network with Profile Encoding

Mingxin Wang,Yingnan Zheng,Binbin Wang,Zhuofu Deng,Hegazy Rezk

doi:10.1155/2021/6661798

Abstract

Household load forecasting provides great challenges as a result of high uncertainty in individual consumption of load profile. Traditional models based on machine learning tried to explore uncertainty depending on clustering, spectral analysis, and sparse coding with hand craft features. Recently, deep learning skills like recurrent neural network attempt to learn the uncertainty with one-hot encoding which is too simple and not efficient. In this paper, for the first time, we proposed a multitask deep convolutional neural network for household load forecasting. The baseline of one branch is built on multiscale dilated convolutions for load forecasting. The other branch based on deep convolutional autoencoder is responsible for household profile encoding. In addition, an efficient encoding strategy for household profile is designed that serves a novel feature fusion mechanism integrated into forecasting branch. Our proposed network serves an end-to-end manner in training and inference process. Sufficient ablation studies were conducted to demonstrate effectiveness of innovations and great generalization in point and probabilistic load forecasting at household level, which provides a promising prospect in demand response.

Highlights

Smart grid is considered as an electric grid that specializes in delivering electricity in a controlled and intelligent approach from points of generation to consumers, both of which form an integral part of the smart grid when customers are able to modify their purchasing patterns and behavior according to the received information, incentives, and disincentives [1,2,3]
As a crucial component of demand response (DR), load forecasting is categorized with different horizon: very shortterm load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF), and longterm load forecasting (LTLF) [9]
We propose a multitask convolutional neural network with household profile encoding (MCNNHPC). e novel encoding branch serves more effective description on household behavior especially focusing on uncertainty

Summary

Introduction

Smart grid is considered as an electric grid that specializes in delivering electricity in a controlled and intelligent approach from points of generation to consumers, both of which form an integral part of the smart grid when customers are able to modify their purchasing patterns and behavior according to the received information, incentives, and disincentives [1,2,3]. Most machine learning skills are competent in learning linear relationships and exploring regular patterns effectively These approaches with hand craft features cannot deal with uncertainty at household level that accounts for a great proportion. Sparse coding becomes preferred at household level that provides each house a profile description and an efficient approach to separate uncertainty, learning, and representing gross patterns of individual consumption [37, 61]. With deeper architecture and sophisticated operations, deep neural networks provide superior ability of learning discriminative features and nonlinear relationships, which benefits extracting uncertainty at household load forecasting [29, 37]. Compared with traditional technique in deep neural network, our proposed method has great predominance to express individual behavior feature and nonlinear correlation in time series analysis.

Methodology

Construction of MCNN-HPC

Implementation

Results and Discussion

Evaluation on test subset