Comparative Analysis of Univariate and Multivariate Models for Solar Irradiance Forecasting
Само за регистроване кориснике
2023
Конференцијски прилог (Објављена верзија)
Метаподаци
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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.
Кључне речи:
global horizontal irradiance (GHI) / machine learning / univariate analysis / multivariate analysis / seasonal forecastИзвор:
2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), 29 June - 01 July 2023, 2023, 155-160Издавач:
- IEEE
Финансирање / пројекти:
- Fellowship support provided by the Ministry of Education, Government of India
DOI: 10.1109/ICEST58410.2023.10187242
ISBN: 979-8-3503-1073-3
Scopus: 2-s2.0-85167873518
Институција/група
Институт техничких наука САНУ / Institute of Technical Sciences of SASATY - 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 .