Weightless Neural Networks Applied to Nonintrusive Load Monitoring

It is well known that energy efficiency plays a key role in ensuring sustainable development. Concerns regarding energy include greenhouse gas emissions, which contribute to global warming, and the possibility of supply interruptions and delivery constraints in some countries and regions of the world. Several studies have suggested that feedback on specific electrical appliances' consumption could be one of the cheapest and most eco-friendly ways to encourage utility customers in energy conservation. Moreover, there is evidence that the best rates of savings are achieved when the appliance load information is delivered directly to the customers' smartphones or dedicated displays inside their homes. Nonintrusive Load Monitoring (NILM) is a technique that estimates the energy consumption of individual appliance loads without requiring the installation of sensors in each appliance. In order to provide feedback directly to the end-user, NILM applications could be embedded in IoT smart devices. However, the amount of computational resources required by NILM algorithms proposed in previous research often discourages embedded applications. On the other hand, the Weightless Neural Network model WiSARD is capable of solving pattern recognition tasks by using a memory-based architecture and some of the most simple computational operations: addition and comparison. Those properties suggest that this particular machine learning model is suited for efficiently solving NILM problem. This paper describes and evaluates a new approach to NILM in which the electric loads are disaggregated by using the Weightless Neural Network model WiSARD. Experimental results using the Brazilian Appliance Dataset (BRAD) indicate that it is feasible to embed WiSARD-based NILM algorithms in low-cost IoT smart energy meters.