Combining Wavelet Transform and Support Vector Regression Model for Day-Ahead Peak Load Forecasting in the Greek Power System

被引：0

作者：

Panapakidis, Ioannis P. ^{[1
]}

Christoforidis, Georgios C. ^{[2
]}

Asimopoulos, Nikolaos ^{[2
]}

Dagoumas, Athanasios S. ^{[3
]}

机构：

[1] TEI Thessaly, Dept Elect Engn, Larisa, Greece

[2] Western Macedonia Univ Appl Sci, Dept Elect Engn, Kozani, Greece

[3] Univ Piraeus, Sch Econ Business & Int Studies, Piraeus, Greece

来源：

2017 1ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2017 17TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE) | 2017年

关键词：

Greek power system; peak load; short-term load forecasting; support vector regression; ELECTRICITY DEMAND; NEURAL-NETWORK; PREDICTION;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Day-ahead peak load forecasting is an essential tool for generation units scheduling, unit commitment and generally, in power systems operation in short-term horizon. The scope of the present study is to develop a robust peak load forecasting model for the power system of Greece. The peak load series is decomposed via the Discrete Wavelet Transform (DWT) into low-frequency and high-frequency subseries in the wavelet domain. For each subseries the Support Vector Regression (SVR) model is trained and applied. The final peak load series is obtained by the inverse DWT. The proposed approach corresponds to low execution time and robust performance and thus, can be a valuable tool for utilities, systems operators and others.

引用

页数：6

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