Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

Da Liu,Zhitao Guan,Shuxia Yang,Yanan Wei

doi:10.1155/2013/648101

Da Liu, Zhitao Guan + Show 2 more

Open Access

https://doi.org/10.1155/2013/648101

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Abstract

A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM) is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM), USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM), generalized regression neural networks (GRNN), day-ahead modeling, and self-organized map (SOM) similar days modeling.

Highlights

Since the 1990s, the monopoly vertically integrated utilities of electric power industries around the world have been deregulated into competitive markets, aiming to break monopoly and increase operation efficiency
The intervals in which the error of each forecasting locates form the sequence of observations or the emission sequence; the forecasting abilities of the individual models are regarded as the state of Hidden Markov model (HMM)
This paper proposes a comprehensive combined forecast technique for day-ahead price by HMM

Summary

Introduction

Since the 1990s, the monopoly vertically integrated utilities of electric power industries around the world have been deregulated into competitive markets, aiming to break monopoly and increase operation efficiency. A hybrid model with support vector machines (SVM) to capture the nonlinear patterns and ARIMA to solve the residuals regression estimation problems was proposed in [9] showing the great potential of hybrid modeling Another hybrid model combining SVM and GARCH was developed in [10] to forecast the day-ahead price of the PJM market. In this paper a hybrid method consisting of a combining model with adaptive weights based circumstance and an error calibration technique was proposed to forecast the day-ahead electricity price. Several individual models were developed to forecast electricity price, respectively; their performances under different circumstances were evaluated to build Hidden Markov models (HMMs). Together with the general past performance of the individual models, the state sequences of the HMMs were proposed to decide the combining weights; the emission sequences of HMMs were exploited to calibrate the errors by the combining model.

Principle of Combined Forecast with Weights Selected by HMM

Approach of Combined Forecasting and Error Calibration by HMM

Numerical Results

Conclusions