Approach to Non-Intrusive Load Monitoring using Factorial Hidden Markov Model

What we measure, we can improve. In accordance to this approach, Indian Institute of Science (HSc), Bangalore has developed a Micro-grid Monitoring System in the campus through the installation of Smart Meters, covering almost 250 nodes including substations, centers, departments, administration, hostels and other utilities. This will help the institute in various ways such as capacity planning, substation loading, phase imbalance correction, over-voltage monitoring, billing and so on. Smart Meters measure the power consumption at a single point in the building giving a picture of the energy consumption of the building as a whole. It is necessary to also understand the scenario of the constituent loads at the point where the smart meter is installed so that ways could be found to reduce consumption. Personalised, concise and reliable feedback providing appliance level breakdown of energy consumption in the premises is the key in implementing energy efficiency programs. Taking this into consideration the area of Non Intrusive Load Monitoring (NILM) was explored. In NILM the aggregate smart meter data is separated into constituent loads by machine learning techniques. The NILM system is trained through previous data sets and then the algorithm will disaggregate the total power into individual appliances based on its experience. A benchmark NILM algorithm called Factorial Hidden Markov Model was used for proper load disaggregation. Finally an attempt was made to develop a Smartphone app to visualize results and bring the data to the people.