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        • Universidad Jorge Tadeo Lozano (Colombia)
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        Electrical load prediction of healthcare buildings through single and ensemble learning

        Registro en:
        2352-4847
        https://doi.org/10.1016/j.egyr.2020.10.005
        http://hdl.handle.net/20.500.12010/14568
        https://doi.org/10.1016/j.egyr.2020.10.005
        http://repositorioslatinoamericanos.uchile.cl/handle/2250/3494134
        Autor
        Cao, Lingyan
        Li, Yongkui
        Zhang, Jiansong
        Jiang, Yi
        Han, Yilong
        Wei, Jianjun
        Institución
        • Universidad Jorge Tadeo Lozano (Colombia)
        Resumen
        Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models.
        Materias
        Healthcare buildings
        Load prediction
        Ensemble model machine learning
        XGBoost
        Random forest

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        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018