Abstract
Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k-length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.
Highlights
Energy is the material basis for the progress and development of human society
The main contributions of this paper are as follows: (1) We proposed a multi-chain energy disaggregation method that considers the relationship between appliances for energy disaggregation and constructs a separate energy disaggregation chain for each appliance; (2) We proposed two methods to reduce the complexity of the search for MC-Non-intrusive load monitoring (NILM) structure; (3) Our experimental results demonstrated that the multi-chain disaggregation method for NILM (MC-NILM) method is a general framework to leverage the existing NILM algorithms as sub-models and improve the overall performance of the original algorithms
We first evaluated the influence of the maximum length of chains on the performance of MC-NILM
Summary
Energy is the material basis for the progress and development of human society. Electricity is a very convenient way to transfer energy, and it has been adapted to a huge and growing number of uses. Most researchers input mains data into a specific model, and the model outputs the inferred value of the power of the target appliance. Due to the high time cost of using the brute-force method to obtain the MC-NILM with the best performance, this article proposes two solutions to reduce complexity. The second approach is to evaluate the relative position of each pair of sub-models in a chain for a target appliance and use this information to guide the searching of chain structure with a graph-based algorithm (GBA). (2) We proposed two methods to reduce the complexity of the search for MC-NILM structure; (3) Our experimental results demonstrated that the MC-NILM method is a general framework to leverage the existing NILM algorithms as sub-models and improve the overall performance of the original algorithms.
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