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Open AccessArticle

Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings

1
Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas—ESPE, 171-5-231B Sangolquí, Ecuador
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Department of Signal Theory and Communications, Rey Juan Carlos University, 28943 Fuenlabrada, Spain
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Wisenet Signal Processing & Wireless Networks laboratory, University of Agder, 4876 Grimstad, Norway
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Center for Computational Simulation, Universidad Politécnica de Madrid; Boadilla, 28223 Madrid, Spain
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Hospital Universitario de Fuenlabrada, 28492 Fuenlabrada, Spain
*
Author to whom correspondence should be addressed.
Energies 2018, 11(3), 493; https://doi.org/10.3390/en11030493
Received: 28 January 2018 / Revised: 13 February 2018 / Accepted: 22 February 2018 / Published: 26 February 2018
Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels. View Full-Text
Keywords: electrical load forecasting; principal component analysis; orthonormal partial least squares; unsupervised processing; ensemble; healthcare buildings; power consumption electrical load forecasting; principal component analysis; orthonormal partial least squares; unsupervised processing; ensemble; healthcare buildings; power consumption
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Gordillo-Orquera , R.; Lopez-Ramos, L.M.; Muñoz-Romero, S.; Iglesias-Casarrubios, P.; Arcos-Avilés, D.; Marques, A.G.; Rojo-Álvarez, J.L. Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings. Energies 2018, 11, 493.

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