Plug and Play! A Simple, Universal Model for Energy Disaggregation

TL;DR

This paper introduces a simple, universal energy disaggregation model called SSER that uses appliance knowledge and sparsity to accurately separate individual energy consumption from aggregated data without complex machine learning.

Contribution

The paper proposes a novel Sparse Switching Event Recovering (SSER) method that avoids reliance on appliance signatures or state transition data, using total variation minimization for effective disaggregation.

Findings

SSER outperforms state-of-the-art methods in detection accuracy.

SSER has lower computational overhead.

The parallel algorithm speeds up the disaggregation process.

Abstract

Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or auxiliary equipments, we make use of easily accessible knowledge of appliances and the sparsity of the switching events to design a Sparse Switching Event Recovering (SSER) method. By minimizing the total variation (TV) of the (sparse) event matrix, SSER can effectively recover the individual energy consumption values from the aggregated ones. To speed up the process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve the problem in active epochs of appliance activities in parallel. Using real-world trace data, we compare the performance of our method with that of the state-of-the-art solutions, including Least Square Estimation (LSE) and iterative Hidden Markov Model (HMM). The results show that our approach has an overall higher detection accuracy and a smaller overhead.