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제안 방법
- ;Short term future price information is important for buyers and sellers in the wholesale electricity market for their optimal operation and risk management This paper investigated forecasting accuracy of four different time-series methods to predict the 24 hourly system marginal prices of the real time wholesale electricity maifcet in the Korean CBP. Model estimation has been carried out using hourly data from the Korean CBP in April 2006.
- The purpose of this study is to estimate the models of real-time hourly wholesale electricity prices of the South Korean CBP market, and to test their performance, which has never been investigated to date. Since the restructuring of the electricity industry around the world during the past 15 years, researchers have used various models to capture the highly volatile characteristics of wholesale electricity prices.
- This paper investigates the accuracy of four forecas ting models for hourly real-time prices of the South Korean wholesale electricity markets for April 2006 an d compares their performance in terms of in-sample a nd out-of-sample forecasting. The estimated models a re ARIMA, transfer function, dynamic regression, and EGARCH.
- This study analyzes hourly real time market clearing prices for the Korean CBP market during April 2006. The data is available on KPX website.
대상 데이터
- is explained below. The final ARMA model is: (1 - 刼甘妇&汨皿)ph=c+Q-eE-e汨2)(12 24BZ4)Ch (2). The ARMA estimated model (2) for April 2006 depends on previous values of prices as a product of 3 terms: 1 hour ago, 1 day ago and 1 week ago.
이론/모형
- This study follows the methodology proposed by Conteras et al (2002), Nogales et al (2003), and Conejo et al (2005) to estimate forecasting models for wholesale competitive electricity market prices in CBP .In order to solve the serial correlation problems inherent in the price series, they adopt a recursive scheme as follows.
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