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Fellowship support provided by the Ministry of Education, Government of India

Authors

Publications

Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting

Singh, Suraj Kumar; Yadav, Sachin; Batas Bjelić, Ilija; Singh, Rhythm

(IEEE, 2023) 

TY  - CONF
AU  - Singh, Suraj Kumar
AU  - Yadav, Sachin
AU  - Batas Bjelić, Ilija
AU  - Singh, Rhythm
PY  - 2023
UR  - https://dais.sanu.ac.rs/123456789/15197
AB  - The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.
PB  - IEEE
C3  - 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023
T1  - Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting
SP  - 155
EP  - 160
DO  - 10.1109/ICEST58410.2023.10187242
UR  - https://hdl.handle.net/21.15107/rcub_dais_15197
ER  - 
@conference{
author = "Singh, Suraj Kumar and Yadav, Sachin and Batas Bjelić, Ilija and Singh, Rhythm",
year = "2023",
abstract = "The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.",
publisher = "IEEE",
journal = "2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023",
title = "Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting",
pages = "155-160",
doi = "10.1109/ICEST58410.2023.10187242",
url = "https://hdl.handle.net/21.15107/rcub_dais_15197"
}
Singh, S. K., Yadav, S., Batas Bjelić, I.,& Singh, R.. (2023). Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting. in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023
IEEE., 155-160.
https://doi.org/10.1109/ICEST58410.2023.10187242
https://hdl.handle.net/21.15107/rcub_dais_15197
Singh SK, Yadav S, Batas Bjelić I, Singh R. Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting. in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023. 2023;:155-160.
doi:10.1109/ICEST58410.2023.10187242
https://hdl.handle.net/21.15107/rcub_dais_15197 .
Singh, Suraj Kumar, Yadav, Sachin, Batas Bjelić, Ilija, Singh, Rhythm, "Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting" in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023 (2023):155-160,
https://doi.org/10.1109/ICEST58410.2023.10187242 .,
https://hdl.handle.net/21.15107/rcub_dais_15197 .

Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting

Singh, Suraj Kumar; Yadav, Sachin; Batas Bjelić, Ilija; Singh, Rhythm

(IEEE, 2023) 

TY  - CONF
AU  - Singh, Suraj Kumar
AU  - Yadav, Sachin
AU  - Batas Bjelić, Ilija
AU  - Singh, Rhythm
PY  - 2023
UR  - https://dais.sanu.ac.rs/123456789/15198
AB  - The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.
PB  - IEEE
C3  - 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023
T1  - Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting
SP  - 155
EP  - 160
DO  - 10.1109/ICEST58410.2023.10187242
UR  - https://hdl.handle.net/21.15107/rcub_dais_15198
ER  - 
@conference{
author = "Singh, Suraj Kumar and Yadav, Sachin and Batas Bjelić, Ilija and Singh, Rhythm",
year = "2023",
abstract = "The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.",
publisher = "IEEE",
journal = "2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023",
title = "Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting",
pages = "155-160",
doi = "10.1109/ICEST58410.2023.10187242",
url = "https://hdl.handle.net/21.15107/rcub_dais_15198"
}
Singh, S. K., Yadav, S., Batas Bjelić, I.,& Singh, R.. (2023). Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting. in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023
IEEE., 155-160.
https://doi.org/10.1109/ICEST58410.2023.10187242
https://hdl.handle.net/21.15107/rcub_dais_15198
Singh SK, Yadav S, Batas Bjelić I, Singh R. Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting. in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023. 2023;:155-160.
doi:10.1109/ICEST58410.2023.10187242
https://hdl.handle.net/21.15107/rcub_dais_15198 .
Singh, Suraj Kumar, Yadav, Sachin, Batas Bjelić, Ilija, Singh, Rhythm, "Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting" in 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST),  29 June - 01 July 2023 (2023):155-160,
https://doi.org/10.1109/ICEST58410.2023.10187242 .,
https://hdl.handle.net/21.15107/rcub_dais_15198 .