Mid-long Term Load Forecasting Using Hidden Markov Model

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Abstract

This paper presents Hidden Markov Models (HMM) approach for mid-long term load forecasting. HMM has been extensively used for pattern recognition and classification problems because of its proven suitability for modeling dynamic systems. However, using HMM for predicting is not straightforward. Here we use only one HMM that is trained on the past dataset of the chosen load data. The trained HMM is used to search for the variable of interest behavioral data pattern from the past dataset. By interpolating the neighboring values of these datasets forecasts are prepared. The results obtained using HMM are encouraging and HMM offers a new paradigm for load forecasting, an area that has been of much research interest lately.

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