Home appliance load modeling from aggregated smart meter data
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This document presents a model called explicit-duration hidden Markov model with differential observations (edhmm-diff) for estimating individual home appliance loads from aggregated smart meter data. It addresses the challenge of load disaggregation, forecasting, and demand response without requiring sub-metering of individual appliances. Experiments show that the proposed model effectively models major home appliance loads using both synthetic and real data.
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Home appliance load modeling from aggregated smart meter data
- 1. HOME APPLIANCE LOAD MODELING FROM AGGREGATED SMART METER DATA ABSTRACT With recent developments in the infrastructure of smart meters and smart grid, more electric power data is available and allows real-time easy data access. Modeling individual home appliance loads is important for tasks such as non-intrusive load disaggregation, load forecasting, and demand response support. Previous methods usually require sub-metering individual appliances in a home separately to determine the appliance models, which may not be practical, since we may only be able to observe aggregated real power signals for the entire-home through smart meters deployed in the field. In this paper, we propose a model, named Explicit-Duration Hidden Markov Model with differential observations (EDHMM-diff), for detecting and estimating individual home appliance loads from aggregated power signals collected by ordinary smart meters. Experiments on synthetic data and real data demonstrate that the EDHMM-diff model and the specialized forward-backward algorithm can effectively model major home appliance loads.