An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting
AbstractHigh quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Ni, Q.; Zhuang, S.; Sheng, H.; Wang, S.; Xiao, J. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting. Energies 2017, 10, 1669.
Ni Q, Zhuang S, Sheng H, Wang S, Xiao J. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting. Energies. 2017; 10(10):1669.Chicago/Turabian Style
Ni, Qiang; Zhuang, Shengxian; Sheng, Hanmin; Wang, Song; Xiao, Jian. 2017. "An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting." Energies 10, no. 10: 1669.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.