A Model for Estimating the Operating Status of Individual Home Appliances Based on Bayesian Estimation from Main Power Consumption

Abstract

The household electricity consumption data contains information on the lifestyles of each household unit, which is useful for marketing of consumer durables and services for households. In general, however, only the main power consumption can be metered using a single smart meter for each residence, and the operating status of home appliances is unknown from the companies' viewpoint. Therefore, there have been several attempts to estimate the electricity consumption of each home appliance using dis-aggregation technology. However, there have been cost problems in practical use for exact estimation, such as the need for additional sensors. In this study, the authors formulate a problem specialized for estimating the operation and non-operation of home appliances using observed main energy data as household attribute information for marketing purposes. They then propose a state estimation model in a snapshot using the assumption of normal distributions for the electricity consumption of home appliances. Simulation experiments in a virtual housing environment show that the proposed model has an effective rate of correct answers in practical use. Additionally, a relationship between the proposed method and the prior probability distribution of device states that serve as input for the estimation is shown. The proposed model is a low-cost method for estimating household attributes that do not require additional sensors, and is expected to be used as basic information for marketing strategies.