Abstract
The features of electricity prices in one period are different from that in another period. Furthermore, the price of weekday is generally higher than that of weekend. A period-decoupled short-term electricity price forecasting method based on separating weekend is presented. The prices of the same time period in each day form the period-decoupled price sequence, then each period-decoupled price sequence is separated into one weekday price sequence and one weekend price sequence. Thus some electricity price subsequences are obtained. The generalized regression neural network (GRNN) based on wavelet analysis is applied to forecast the day-ahead price for each electricity price subsequence respectively, and the hourly price of the forecasted day is obtained by seriating the forecasted prices of each subsequence. The proposed method is applied to forecast electricity price for Spanish electricity market in numerical examples, and it is compared with four existing methods. The comparisons of forecasting results indicate that the proposed method can provide more accurate predictions.
| Original language | English |
|---|---|
| Pages (from-to) | 4-8 |
| Number of pages | 5 |
| Journal | Dianli Xitong Zidonghua/Automation of Electric Power Systems |
| Volume | 31 |
| Issue number | 3 |
| State | Published - Feb 2007 |
Keywords
- Electricity market
- Electricity price forecasting
- Generalized regression neural network
- Wavelet analysis