Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

被引：34
作者：
Department of Business Administration, Rensselaer Polytechnic Institute, Troy, NY, United States [1 ]
不详 [2 ]
机构：
[1] Department of Business Administration, Rensselaer Polytechnic Institute, Troy, NY
[2] Constellation New Energy, Baltimore, 22202, 111 Market Place
来源：
Util. Policy | 2008年 / 1卷 / 39-48期
关键词：
Electricity price forecasting; Fuzzy logic; Fuzzy reasoning; OEM; Statistical models;
D O I：
10.1016/j.jup.2007.10.002
中图分类号：
学科分类号：
摘要：
The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. © 2007 Elsevier Ltd. All rights reserved.
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页码：39 / 48
页数：9
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