An Empirical Study with A Low-Cost Strategy for Improving the Energy Disaggregation via Questionnaire Survey


Chun-peng Chang, Wen-Jen Ho, Yung-chieh Hung, Kuei-Chun Chiang and Bill Zhao, Institute for Information Industry, Taiwan


Based on neural network and machine learning, we apply the energy disaggregation for both classification (prediction on usage time) and estimation (prediction on usage amount) on 150 AMI (Advanced Metering Infrastructure) smart meters and a small amount of HEMS (Home Energy Management System) smart plugs in a community in New Taipei City, Taiwan. The aim of this paper is to clarify how we lower the cost, obtain the model of appliance usage from only a small portion of households, improve it with simple questionnaire, and generalize it for prediction on collective households. Our investigation demonstrates the benefits and various possibilities for power suppliers and the government, and won the Elite Award in the Presidential Hackathon 2020, Taiwan.


Energy Disaggregation, Non-intrusive Load Monitoring, Deep Learning, Autoencoder.

Full Text  Volume 10, Number 12