A Multi-objective Non-intrusive Load Monitoring Method Based on Deep Learning

2019 4th Asia Conference on Power and Electrical Engineering

能源学;电工学

Yu, Hua^1 ; Jiang, Zhiwei^1 ; Li, Yaping^2 ; Zhou, Jing^2 ; Wang, Ke^2 ; Cheng, Zifeng^1 ; Gu, Qing^1

Department of Computer Science and Technology, Nanjing University, Nanjing

210023, China^1

China Electric Power Research Institute Co., Ltd. (Nanjing), Nanjing

210003, China^2

关键词: Absolute error;    Electrical appliances;    Fine-grained power;    Multi objective;    Multi-objective modeling;    Nonintrusive load monitoring;    Total errors;    Usage habits;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/486/1/012110/pdf
DOI  :  10.1088/1757-899X/486/1/012110

来源: IOP

【 摘 要 】

Fine-grained power data are used to characterize user's behavior in smart grid, Non-intrusive load monitoring can effectively separate the load of a single electrical appliance from the whole energy consumption of a dwelling, which is beneficial to fully exploit the load potential. In this paper, considering the relationship on usage habits among electrical appliances, the characteristics of energy consumption, we propose a multi-objective modeling method based on deep learning. The multi-objective model is constructed by CNN and LSTM, and the neural network is collaboratively optimized by multi-objective outputs. The experimental results show that the multi-objective model performs better than the other models on the five appliances, reducing the absolute error to less than 10 and the total error of the standardized signal to less than 0.1.