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Title: Variable Generation Power Forecasting as a Big Data Problem

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model and observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.
Authors:
 [1] ;  [1]
  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
Publication Date:
Grant/Contract Number:
EE0006016
Type:
Published Article
Journal Name:
IEEE Transactions on Sustainable Energy
Additional Journal Information:
Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 1949-3029
Publisher:
IEEE
Research Org:
National Center for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 17 WIND ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; big data; power forecasting; solar energy; variable generation; wind energy
OSTI Identifier:
1347273
Alternate Identifier(s):
OSTI ID: 1347274; OSTI ID: 1425149

Haupt, Sue Ellen, and Kosovic, Branko. Variable Generation Power Forecasting as a Big Data Problem. United States: N. p., Web. doi:10.1109/TSTE.2016.2604679.
Haupt, Sue Ellen, & Kosovic, Branko. Variable Generation Power Forecasting as a Big Data Problem. United States. doi:10.1109/TSTE.2016.2604679.
Haupt, Sue Ellen, and Kosovic, Branko. 2016. "Variable Generation Power Forecasting as a Big Data Problem". United States. doi:10.1109/TSTE.2016.2604679.
@article{osti_1347273,
title = {Variable Generation Power Forecasting as a Big Data Problem},
author = {Haupt, Sue Ellen and Kosovic, Branko},
abstractNote = {To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model and observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.},
doi = {10.1109/TSTE.2016.2604679},
journal = {IEEE Transactions on Sustainable Energy},
number = 2,
volume = 8,
place = {United States},
year = {2016},
month = {10}
}