Title

Technical Papers Parallel Session-IV: Short-term stochastic load forecasting using autoregressive integrated moving average models and Hidden Markov Model

Abstract/Description

Load forecasting, particularly short-term load forecasting (STLF) plays a vital role in the economy streaming and tracking of power system. Many stochastic and artificial intelligence techniques haven been used in order to come up with an accurate (less error) short-term load forecast. Here, we introduce a new approach to short-term load forecasting (STLF) using the conventional Hidden Markov Model (HMM) then compare it with Autoregressive Integrated Moving Average (ARIMA) models. Three-dimensional continuous multivariate Gaussian emission probabilities are used in this experiment for HMM. Meanwhile for ARIMA models, different parameters are used for different kinds of dataset. Comparison is done afterwards to the actual load value using MAPE and RMSE.

Location

Theatre 1, Aman Tower

Session Theme

Technical Papers Parallel Session-IV: Artificial Intelligence

Session Type

Parallel Technical Session

Session Chair

Dr. Syeda Saleha Raza

Start Date

31-12-2017 2:20 PM

End Date

31-12-2017 2:40 PM

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Dec 31st, 2:20 PM Dec 31st, 2:40 PM

Technical Papers Parallel Session-IV: Short-term stochastic load forecasting using autoregressive integrated moving average models and Hidden Markov Model

Theatre 1, Aman Tower

Load forecasting, particularly short-term load forecasting (STLF) plays a vital role in the economy streaming and tracking of power system. Many stochastic and artificial intelligence techniques haven been used in order to come up with an accurate (less error) short-term load forecast. Here, we introduce a new approach to short-term load forecasting (STLF) using the conventional Hidden Markov Model (HMM) then compare it with Autoregressive Integrated Moving Average (ARIMA) models. Three-dimensional continuous multivariate Gaussian emission probabilities are used in this experiment for HMM. Meanwhile for ARIMA models, different parameters are used for different kinds of dataset. Comparison is done afterwards to the actual load value using MAPE and RMSE.