Forecasting of global solar insolation using ensemble Kalman filter based clearness index model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole precipitable water content in vertical column of that location, are used. These parameters can be easily...

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Bibliographic Details
Published inCSEE Journal of Power and Energy Systems Vol. 8; no. 4; pp. 1087 - 1096
Main Authors Pravat Kumar Ray, Bidyadhar Subudhi, Ghanim Putrus, Mousa Marzband, Zunaib Ali
Format Journal Article
LanguageEnglish
Published Beijing Chinese Society for Electrical Engineering Journal of Power and Energy Systems 01.07.2022
China electric power research institute
Subjects
Algorithms
Constraint modelling
Correlation coefficient
Correlation coefficients
Forecasting
Global positioning systems
GPS
Insolation
Kalman filters
Moisture content
Water content
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Summary:This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole precipitable water content in vertical column of that location, are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and the central region with the measured values from the same locations. The model has also been validated on a region, from which data was not used during the development of the model. In the model, clearness index coefficients (KT) are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms, namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%, 0.0285 and 0.9866 respectively.
ISSN:2096-0042
2096-0042
DOI:10.17775/CSEEJPES.2021.06230