Font Size: a A A

Electricity Price Forecasting Based On Data Mining And Weighted Regression

Posted on:2004-06-14Degree:MasterType:Thesis
Country:ChinaCandidate:G C YuanFull Text:PDF
GTID:2156360092980299Subject:Electrical Engineering and Automation
Abstract/Summary:PDF Full Text Request
With the deregulation of power industry allover the world, establishing electricity markets to optimize distribution of resources is the trend of power industry. Electricity price issues are the key problems in the markets and how to price the special commodity - electricity is essential for the smooth market operation. So using the relative historic data in predicting the future electricity price is a very meaningful work.Both power system load and price are time series. Theoretically speaking, methods applied to forecast load can be used to forecast price, such as Time Series Analysis, Artificial Neuro Network and Wavelet Transform etc. But electricity price is more difficult to forecast than load because of its inherent characteristics such as volatility and price spikes.Data Mining or Knowledge Discovery in Databases is a new Artificial Intelligence branch developed since1990s'. Since it has the capacity to distill connotative knowledge and information from abundant data, data mining has been used widely in various fields. One of the important techniques of data mining ?similarity search is used in this thesis.This thesis advances a new short-term price forecasting method based on time-series similarity search technique and weighted regression technique after comprehensively analyzes the relative factors. The method can not only forecast electricity price exactly, but also examine and forecast price spikes successfully. Testing on California's electricity market proves this method's effectivity.
Keywords/Search Tags:Electricity Market, Price Forecasting, Price Spikes, Similarity Search, Data Mining
PDF Full Text Request
Related items