Short term solar irradiance forecast based on image processing and cloud motion detection

Soumya Tiwari; Reza Sabzehgar; Mohammad Rasouli

doi:10.1109/TPEC.2019.8662134

Short term solar irradiance forecast based on image processing and cloud motion detection

Soumya Tiwari
, Reza Sabzehgar
, Mohammad Rasouli

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

25 Scopus citations

Abstract

Photovoltaic (PV) grid integration has been the epicenter of research across the globe since their intermittent nature of solar generation can be more predictable. Irradiance forecast using different methods for various time horizons has been the center of attention in the recent literature. In this study, a framework for a very short term irradiance forecast is proposed via combining image processing and machine learning. A series of whole sky images is used for this purpose. Cloud detection and movement tracking are accomplished based on image processing algorithms, future position of the clouds and occlusion to the sun. Then, the irradiance drop is predicted using machine learning algorithms. The effectiveness of the proposed technique is evaluated by the Root Mean Square Error (RMSE) between the actual and forecast values of solar irradiance.

Original language	English (US)
Title of host publication	2019 IEEE Texas Power and Energy Conference, TPEC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538692844
DOIs	https://doi.org/10.1109/TPEC.2019.8662134
State	Published - Mar 6 2019
Event	2019 IEEE Texas Power and Energy Conference, TPEC 2019 - College Station, United States Duration: Feb 7 2019 → Feb 8 2019

Publication series

Name	2019 IEEE Texas Power and Energy Conference, TPEC 2019

Conference

Conference	2019 IEEE Texas Power and Energy Conference, TPEC 2019
Country/Territory	United States
City	College Station
Period	2/7/19 → 2/8/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/TPEC.2019.8662134

Cite this

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title = "Short term solar irradiance forecast based on image processing and cloud motion detection",

abstract = "Photovoltaic (PV) grid integration has been the epicenter of research across the globe since their intermittent nature of solar generation can be more predictable. Irradiance forecast using different methods for various time horizons has been the center of attention in the recent literature. In this study, a framework for a very short term irradiance forecast is proposed via combining image processing and machine learning. A series of whole sky images is used for this purpose. Cloud detection and movement tracking are accomplished based on image processing algorithms, future position of the clouds and occlusion to the sun. Then, the irradiance drop is predicted using machine learning algorithms. The effectiveness of the proposed technique is evaluated by the Root Mean Square Error (RMSE) between the actual and forecast values of solar irradiance.",

author = "Soumya Tiwari and Reza Sabzehgar and Mohammad Rasouli",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Texas Power and Energy Conference, TPEC 2019 ; Conference date: 07-02-2019 Through 08-02-2019",

year = "2019",

month = mar,

day = "6",

doi = "10.1109/TPEC.2019.8662134",

language = "English (US)",

series = "2019 IEEE Texas Power and Energy Conference, TPEC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE Texas Power and Energy Conference, TPEC 2019",

address = "United States",

}

Tiwari, S, Sabzehgar, R & Rasouli, M 2019, Short term solar irradiance forecast based on image processing and cloud motion detection. in 2019 IEEE Texas Power and Energy Conference, TPEC 2019., 8662134, 2019 IEEE Texas Power and Energy Conference, TPEC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Texas Power and Energy Conference, TPEC 2019, College Station, United States, 2/7/19. https://doi.org/10.1109/TPEC.2019.8662134

Short term solar irradiance forecast based on image processing and cloud motion detection. / Tiwari, Soumya; Sabzehgar, Reza; Rasouli, Mohammad.
2019 IEEE Texas Power and Energy Conference, TPEC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8662134 (2019 IEEE Texas Power and Energy Conference, TPEC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Short term solar irradiance forecast based on image processing and cloud motion detection

AU - Tiwari, Soumya

AU - Sabzehgar, Reza

AU - Rasouli, Mohammad

PY - 2019/3/6

Y1 - 2019/3/6

N2 - Photovoltaic (PV) grid integration has been the epicenter of research across the globe since their intermittent nature of solar generation can be more predictable. Irradiance forecast using different methods for various time horizons has been the center of attention in the recent literature. In this study, a framework for a very short term irradiance forecast is proposed via combining image processing and machine learning. A series of whole sky images is used for this purpose. Cloud detection and movement tracking are accomplished based on image processing algorithms, future position of the clouds and occlusion to the sun. Then, the irradiance drop is predicted using machine learning algorithms. The effectiveness of the proposed technique is evaluated by the Root Mean Square Error (RMSE) between the actual and forecast values of solar irradiance.

AB - Photovoltaic (PV) grid integration has been the epicenter of research across the globe since their intermittent nature of solar generation can be more predictable. Irradiance forecast using different methods for various time horizons has been the center of attention in the recent literature. In this study, a framework for a very short term irradiance forecast is proposed via combining image processing and machine learning. A series of whole sky images is used for this purpose. Cloud detection and movement tracking are accomplished based on image processing algorithms, future position of the clouds and occlusion to the sun. Then, the irradiance drop is predicted using machine learning algorithms. The effectiveness of the proposed technique is evaluated by the Root Mean Square Error (RMSE) between the actual and forecast values of solar irradiance.

UR - https://www.scopus.com/pages/publications/85063903046

UR - https://www.scopus.com/pages/publications/85063903046#tab=citedBy

U2 - 10.1109/TPEC.2019.8662134

DO - 10.1109/TPEC.2019.8662134

M3 - Conference contribution

AN - SCOPUS:85063903046

T3 - 2019 IEEE Texas Power and Energy Conference, TPEC 2019

BT - 2019 IEEE Texas Power and Energy Conference, TPEC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Texas Power and Energy Conference, TPEC 2019

Y2 - 7 February 2019 through 8 February 2019

ER -

Short term solar irradiance forecast based on image processing and cloud motion detection

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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