Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings

Rodolfo Gordillo-Orquera, Luis Miguel Lopez-Ramos, Sergio Muñoz-Romero, Paz Iglesias-Casarrubios, Diego Arcos-Avilés, Antonio G. Marques and José Luis Rojo-Álvarez
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Rodolfo Gordillo-Orquera: Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas—ESPE, 171-5-231B Sangolquí, Ecuador
Luis Miguel Lopez-Ramos: Department of Signal Theory and Communications, Rey Juan Carlos University, 28943 Fuenlabrada, Spain
Sergio Muñoz-Romero: Department of Signal Theory and Communications, Rey Juan Carlos University, 28943 Fuenlabrada, Spain
Paz Iglesias-Casarrubios: Hospital Universitario de Fuenlabrada, 28492 Fuenlabrada, Spain
Diego Arcos-Avilés: Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas—ESPE, 171-5-231B Sangolquí, Ecuador
Antonio G. Marques: Department of Signal Theory and Communications, Rey Juan Carlos University, 28943 Fuenlabrada, Spain
José Luis Rojo-Álvarez: Department of Signal Theory and Communications, Rey Juan Carlos University, 28943 Fuenlabrada, Spain

Energies, 2018, vol. 11, issue 3, 1-18

Abstract: 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.

Keywords: electrical load forecasting; principal component analysis; orthonormal partial least squares; unsupervised processing; ensemble; healthcare buildings; power consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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