Abstract
Non-Intrusive Load Monitoring (NILM) has be-come popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37%and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.
Highlights
Using energy in an efficient manner has become one of the highest concerns for both utility and end-users nowadays, as the world is facing challenging problems including depletion of natural resources and emissions of environmentally hazardous gases
This study provides a base for Non-Intrusive Load Monitoring (NILM) researchers to compare their results, especially for ENERTALK dataset which is a relatively recent dataset with large number of houses
Overall average performance F1-score is 84.58% which again confirms the robustness of point-to-point Convolutional Neural Networks (CNN) modelII in single target setting for power disaggregation
Summary
Using energy in an efficient manner has become one of the highest concerns for both utility and end-users nowadays, as the world is facing challenging problems including depletion of natural resources and emissions of environmentally hazardous gases. NILM is a method that can deduce energy consumption of individual appliances from aggregated smart meter power data recorded at a single source This process is based on software techniques and requires effective and efficient techniques for successful disaggregation. There may be multiple predictions for a single time point in seq2seq which is redundant and calculation of mean is necessary for the final prediction Considering all these points, this work formulates the disaggregation problem as a direct point to point regression problem on the motivation that it will retain the granularity of consumption information that will help the generalization capability and knowledge transfer in the disaggregation domain which is demonstrated in the experiment section.
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