Abstract
This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data.
| Original language | English |
|---|---|
| Article number | 7448478 |
| Pages (from-to) | 817-819 |
| Number of pages | 3 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 32 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
Keywords
- Electricity price
- extreme learning machine
- NSGA-II
- prediction intervals
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering