Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

Jie Zhang, Brian Hodge, Siyuan Lu, Youngdeok Hwang, Ildar Khabibrakhmanov, Fernando Marianno, Xiaoyan Shao, Hendrik Hamann

Research output: Contribution to conferencePaperpeer-review

49 Scopus Citations

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.

Original languageAmerican English
Pages283-290
Number of pages8
DOIs
StatePublished - 16 Nov 2015
EventEuropean Control Conference, ECC 2015 - Linz, Austria
Duration: 15 Jul 201517 Jul 2015

Conference

ConferenceEuropean Control Conference, ECC 2015
Country/TerritoryAustria
CityLinz
Period15/07/1517/07/15

Bibliographical note

Publisher Copyright:
© 2015 EUCA.

NREL Publication Number

  • NREL/CP-5D00-64085

Keywords

  • forecasting
  • National Renewable Energy Laboratory
  • NREL
  • renewables
  • solar
  • wind

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