DOE Patents title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Solar forecasting using machine learned cloudiness classification

Abstract

Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Inventors:
; ; ;
Issue Date:
Research Org.:
International Business Machines Corp., Armonk, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1892823
Patent Number(s):
11300707
Application Number:
15/226,445
Assignee:
International Business Machines Corporation (Armonk, NY)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01W - METEOROLOGY
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
DOE Contract Number:  
EE0006017
Resource Type:
Patent
Resource Relation:
Patent File Date: 08/02/2016
Country of Publication:
United States
Language:
English

Citation Formats

Hamann, Hendrik F., Khabibrakhmanov, Ildar, Kim, Younghun, and Lu, Siyuan. Solar forecasting using machine learned cloudiness classification. United States: N. p., 2022. Web.
Hamann, Hendrik F., Khabibrakhmanov, Ildar, Kim, Younghun, & Lu, Siyuan. Solar forecasting using machine learned cloudiness classification. United States.
Hamann, Hendrik F., Khabibrakhmanov, Ildar, Kim, Younghun, and Lu, Siyuan. Tue . "Solar forecasting using machine learned cloudiness classification". United States. https://www.osti.gov/servlets/purl/1892823.
@article{osti_1892823,
title = {Solar forecasting using machine learned cloudiness classification},
author = {Hamann, Hendrik F. and Khabibrakhmanov, Ildar and Kim, Younghun and Lu, Siyuan},
abstractNote = {Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {4}
}

Works referenced in this record:

Multi-Model Blending
patent-application, December 2015


Method and System for Solar Power Forecasting
patent-application, September 2018


A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
journal, July 2014


Predicting Solar Irradiance Using Time Series Neural Networks
journal, January 2014


Solar radiation forecast with machine learning
conference, July 2016


Operating a solar power generating system
patent, October 2018


Weather and Satellite Model for Estimating Solar Irradiance
patent-application, June 2013


Systems and Methods for Forecasting Solar Power
patent-application, November 2011


Machine Learning Approach for Analysis and Prediction of Cloud Particle Size and Shape Distribution
patent-application, October 2014


Predicting Solar Power Generation Using Semi-Supervised Learning
patent-application, October 2017


New types of simple non-linear models to compute solar global irradiance from cloud cover amount
journal, September 2014


Long-term solar generation forecasting
conference, May 2016


Apparatus and method for predicting solar irradiance variation
patent, December 2014


System and Methods for Model Based Solar Power Management
patent-application, May 2014


Systems and Methods for Simulating Time Phased Solar Irradiance Plots
patent-application, July 2015