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Title: A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

Abstract

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

Authors:
 [1]
  1. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
Publication Date:
Research Org.:
IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1395344
Report Number(s):
DE-EE-0006017
DOE Contract Number:  
EE0006017
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hamann, Hendrik F. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology. United States: N. p., 2017. Web. doi:10.2172/1395344.
Hamann, Hendrik F. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology. United States. doi:10.2172/1395344.
Hamann, Hendrik F. Wed . "A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology". United States. doi:10.2172/1395344. https://www.osti.gov/servlets/purl/1395344.
@article{osti_1395344,
title = {A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology},
author = {Hamann, Hendrik F.},
abstractNote = {The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.},
doi = {10.2172/1395344},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed May 31 00:00:00 EDT 2017},
month = {Wed May 31 00:00:00 EDT 2017}
}

Technical Report:

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