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An effective electrical load disaggregation approach for low-sampled smart meter data

Smart meters deployment plays an important role in electric utilities' digital transformation. Though smart meters were employed for remote and efficient billing, smart meter data could be further used to glean more information about the customers using Load Disaggregation. Electrical Load Disaggregation or Non-intrusive Appliance Monitoring (NILM) is about breaking down the total energy consumption measured by smart meters into appliance-specific consumption patterns. NILM helps in identifying various appliances used in the home, their time of usage and their respective energy consumption. This information helps different players (aggregators, retailers, and large enterprises) in the electricity supply chain for efficient power distribution and management. NILM is an inverse problem and very challenging to solve as it requires inferring the multiple causes by observing the cumulative effect. The problem becomes even more difficult with the lower sampling rate (> 10 seconds) as all the appliance-specific signatures are diffused due to the averaging effect of the lower sampling rate. Though there are algorithms based on different techniques available in the state-of-art, the challenge of different power ratings of the same appliance, and variation of patterns due to the different makes is not addressed to a sufficient extent for practical use. Apart from this, the increase in the number of appliances also increases the complexity of a few of the existing techniques making them difficult to be used in practice. In this regard, we present a comprehensive and practical approach for solving load disaggregation using smart meter data in addition to available meta-data like dwelling type, weather etc. The proposed solution is an unsupervised approach which involves a series of steps that include composite event detection, iterative event pairing with dynamic thresholds, clustering and followed by a rule-based system for appliance labelling and consumption estimation. Annual consumption data is used in correlation with the weather to identify the presence of any seasonal loads and the same is used for interference cancellation from similar loads in the appliance labelling stage. The validation of the proposed approach was carried out using open data sources as well as the actual customer data. The appliance identification and consumption estimation accuracy are on par with the state-of-the-art solutions besides having practical applicability.