Predicting the State of Energy Utilisation Based on Hidden Markov Model (HMM)

This paper presents the utilisation of the hidden Markov model (HMM) to predict the state of energy utilisation of light. A dataset of light energy utilisation is used to develop an HMM model with two (2) components/states. The trained model is tested on a separate set of data to investigate the performance of the developed model. The results showed that the model can predict the state of energy utilisation of the lights in a house. This prediction can be used to detect the occupation in a room which can be useful for demand side management.