Name
Title | ||
|---|---|---|
Machine learning based multi-physical-model blending for enhancing renewable energy forecast - improvement via situation dependent error correction |
Abstract | ||
|---|---|---|
With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ECC.2015.7330558 | 2015 European Control Conference (ECC) |
Keywords | Field | DocType |
solar irradiance/power forecast accuracy,model averaging,multimodel ensemble,weather situation,substantial dependence,analysis of variance,atmospheric state parameter,solar/wind power forecast,meteorological model,energy forecasting,energy supply mix,wind energy,solar energy,situation dependent error correction,renewable energy forecast,machine learning based multiphysical-model blending | Renewable energy,Energy forecasting,Error detection and correction,Atmospheric model,Artificial intelligence,Probabilistic forecasting,Energy supply,Solar irradiance,Engineering,Machine learning,Wind power | Conference |
Citations | PageRank | References |
5 | 0.68 | 6 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Lu | 1 | 14 | 6.85 |
| youngdeok hwang | 2 | 5 | 2.03 |
| ildar k khabibrakhmanov | 3 | 5 | 0.68 |
| fernando j marianno | 4 | 5 | 0.68 |
| xiaoyan shao | 5 | 5 | 0.68 |
| jie zhang | 6 | 14 | 7.13 |
| brimathias hodge | 7 | 16 | 5.52 |
| Hendrik F. Hamann | 8 | 24 | 9.28 |