Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods

According to recent studies by the BBC and the Scottish Fire and Rescue Service, malfunctioning appliances, especially white goods, were responsible for almost 12,000 fires in Great Britain in just over 3 years, and almost everyday in 2019. The top three “offenders” are washing machines, tumble dryers and dishwashers, hence we will focus on these, generally challenging to disaggregate, appliances in this paper. The first step towards remotely assessing safety in the house, e.g., due to appliances not being switched off or appliance malfunction, is by detecting appliance state and consumption from the NILM result generated from smart meter data. While supervised NILM methods are expected to perform best on the house they were trained on, this is not necessarily the case with transfer learning on unseen houses; unsupervised NILM may be a better option. However, unsupervised methods in general tend to be affected by the noise in the form of unknown appliances, varying power levels and signatures. We evaluate the robustness of three well-performing (based on prior studies) low-complexity NILM algorithms in order to determine appliance state and consumption: Decision Tree and KNN (supervised) and DBSCAN (unsupervised), as well as different algorithms for preprocessing to mitigate the effect of noisy data. These are tested on two datasets with different levels of noise, namely REFIT and REDD datasets, resampled to 1 min resolution

Performance Evaluation of Superstate HMM with Median Filter For Appliance Energy Disaggregation

Information on electricity consumption is one of the essential elements in terms of regulating the distribution of electricity in smart micro grid. Besides, information on electricity consumption can help consumers carry out an evaluation process to reduce electricity bill costs, which indirectly affect overall energy efficiency. One method in the process of monitoring electricity consumption is Non-Intrusive Load Monitoring (NILM). The main problem in NILM is to determine the energy disaggregation consumed by several equipment by merely performing the retrieval of data from only one measuring point. We used the Superstate Hidden Markov Model as the tool for modelling and analysis. A median data filter to the input data is applied to improve the performance of the disaggregation process. Based on the results of tests conducted using the REDD, the lowest accuracy was 96.69% for all tests performed

Unsupervised energy disaggregation via convolutional sparse coding

In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active or passive, granting the ability to report on the residents' activity and presence without direct interaction. This lays the foundation for applications like non-intrusive health monitoring of private homes. The proposed method is based on minimizing a suitable energy functional, for which the iPALM (inertial proximal alternating linearized minimization) algorithm is employed, demonstrating that various conditions guaranteeing convergence are satisfied. In order to confirm feasibility of the proposed method, experiments on semi-synthetic test data sets and a comparison to existing, supervised methods are provided.Comment: 9 pages, 2 figures, 3 table

Hidden Markov Model based non-intrusive load monitoring using active and reactive power consumption

This work presents a residential appliance disaggregation technique to help achieve the fundamental goal in Non-Intrusive Load Monitoring (NILM) problem i.e. simple breakdown of energy consumption based on the appliance type in a household. The appliances are modeled using Hidden Markov Model by utilizing both their active and reactive power consumption data. The data was recorded by attaching Power Standards Lab PQube measurement device to the appliances. Granularity of the power readings of the disaggregated appliance matches with that of the reading collected for individual device. The accuracy of the model is compared with other models developed using only active power consumption of the appliances. The results using the proposed method are more effective and are found to predict a better output sequence for the appliances compared to model using only active power for modeling loads --Abstract, page iii