Household Energy Disaggregation based on Difference Hidden Markov Model
We aim to address the problem of energy disaggregation into individual appliance contribution for a given household. We do this based on a Machine Learning approach that involves modeling each appliance's load contribution to be a Markov chain. Once the Hidden Markov Model (HMM) is trained, we can get the parameters such as the number of states and their prior probabilities, transition matrix and the emission characteristics for each appliance (each state is modeled as a Gaussian distribution) The disaggregation of the total load is modeled as a difference Hidden Markov Model (difference HMM) based on HMMs for each of the appliance learned previously. Finally, the most probable set of states for all the appliances is obtained using a custom Viterbi algorithm. This approach to energy disaggregation works well for fixed state appliances, as can be seen in the results.