Price forecast in the competitive electricity market by support vector machine

Ciwei Gao, Ettore Bompard, Roberto Napoli and Haozhong Cheng

Physica A: Statistical Mechanics and its Applications, 2007, vol. 382, issue 1, 98-113

Abstract: The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter Ci and εi assigned to each training data, the flexible Ci Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin Rm, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).

Keywords: Electricity market; Flexible Ci SVR; Price forecast; Particle swarm optimization (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:382:y:2007:i:1:p:98-113

DOI: 10.1016/j.physa.2007.03.050

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