Variable Generation Power Forecasting as a Big Data Problem
Abstract
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:
- Publication Date:
- Research Org.:
- National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1347273
- Alternate Identifier(s):
- OSTI ID: 1347274; OSTI ID: 1425149
- Grant/Contract Number:
- EE0006016
- Resource Type:
- Published Article
- Journal Name:
- IEEE Transactions on Sustainable Energy
- Additional Journal Information:
- Journal Name: IEEE Transactions on Sustainable Energy Journal Volume: 8 Journal Issue: 2; Journal ID: ISSN 1949-3029
- Publisher:
- Institute of Electrical and Electronics Engineers
- 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
Citation Formats
Haupt, Sue Ellen, and Kosovic, Branko. Variable Generation Power Forecasting as a Big Data Problem. United States: N. p., 2017.
Web. doi:10.1109/TSTE.2016.2604679.
Haupt, Sue Ellen, & Kosovic, Branko. Variable Generation Power Forecasting as a Big Data Problem. United States. https://doi.org/10.1109/TSTE.2016.2604679
Haupt, Sue Ellen, and Kosovic, Branko. Sat .
"Variable Generation Power Forecasting as a Big Data Problem". United States. https://doi.org/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 = {Sat Apr 01 00:00:00 EDT 2017},
month = {Sat Apr 01 00:00:00 EDT 2017}
}
https://doi.org/10.1109/TSTE.2016.2604679
Search WorldCat to find libraries that may hold this journalWeb of Science