{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/electrical-load-forecasting-using-hybrid-of","title":"Electrical load forecasting using hybrid of extreme gradient boosting and light gradient boosting machine","arxiv_id":null,"date":"2022-03-03","proceeding":"The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021) 2022 3","authors":["Eric Nziyumva","Rong Hu","Chih-Yu Hsu","Jovial Niyogisubizo"],"abstract":"Ensemble learning methods have been used to improve performance accuracy through bias-variance trade-off techniques. However, there is still room to improve. This paper proposes an ensemble model to forecast the electrical load behavior based on a hybrid of Extreme Gradient Boosting (XGBoost) and Light gradient boosting machine (LGBM). Extreme gradient boosting (XGBoost), a Light gradient boosting machine (LGBM) and a hybrid of XGBoost and LGBM models are trained, evaluated, and compared. The experiments show that the proposed model outperforms other methods by reducing more than 1% in mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), and mean absolute error (MAE). The dataset from the Pennsylvania-New Jersey-Maryland interconnection power grid was used to validate the evolutionary capability of the proposed method and the finding of optimal accuracy of the model.","url_abs":"https://doi.org/10.1007/978-981-16-6963-7_95","url_pdf":"https://www.researchgate.net/profile/Jovial-Niyogisubizo/publication/358980612_Electrical_Load_Forecasting_Using_Hybrid_of_Extreme_Gradient_Boosting_and_Light_Gradient_Boosting_Machine/links/652753c43fa934104b17f5d1/Electrical-Load-Forecasting-Using-Hybrid-of-Extreme-Gradient-Boosting-and-Light-Gradient-Boosting-Machine.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"electrical-load-forecasting-using-hybrid-of","repo_url":"https://github.com/jovialniyo93/electrical_load_forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"electrical-engineering","task_name":"Electrical Engineering"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"hybrid-machine-learning","task_name":"Hybrid Machine Learning"},{"task_slug":"load-forecasting","task_name":"Load Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}