Abstract
A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming-based prediction interval construction model for PV power generation is established based on an extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.
| Original language | English |
|---|---|
| Pages (from-to) | 2471-2472 |
| Number of pages | 2 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 32 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 May 2017 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Extreme learning machine
- prediction intervals
- probabilistic forecasting
- PV power
- quantile regression
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
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