Solar forecasting using machine learned cloudiness classification

Hamann, Hendrik F.; Khabibrakhmanov, Ildar; Kim, Younghun; Lu, Siyuan

Solar forecasting using machine learned cloudiness classification

Patent · 12 April 2022

OSTI ID:1892823

Hamann, Hendrik F.; Khabibrakhmanov, Ildar; Kim, Younghun; Lu, Siyuan

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.

Research Organization:: International Business Machines Corp., Armonk, NY (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: EE0006017

Assignee:: International Business Machines Corporation (Armonk, NY)

Patent Number(s):: 11,300,707

Application Number:: 15/226,445

OSTI ID:: 1892823

Country of Publication:: United States

Language:: English

References (9)

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Online 24-h solar power forecasting based on weather type classification using artificial neural network Chen, Changsong; Duan, Shanxu; Cai, Tao Solar Energy, Vol. 85, Issue 11 https://doi.org/10.1016/j.solener.2011.08.027	journal	November 2011
A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output Yang, Hong-Tzer; Huang, Chao-Ming; Huang, Yann-Chang IEEE Transactions on Sustainable Energy, Vol. 5, Issue 3 https://doi.org/10.1109/TSTE.2014.2313600	journal	July 2014
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Long-term solar generation forecasting Alanazi, Mohana; Alanazi, Abdulaziz; Khodaei, Amin 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) https://doi.org/10.1109/TDC.2016.7519883	conference	May 2016
An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records Huang, Jing; Troccoli, Alberto; Coppin, Peter Renewable Energy, Vol. 72 https://doi.org/10.1016/j.renene.2014.07.015	journal	December 2014

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