RT info:eu-repo/semantics/article
T1 Model of monthly electricity consumption of healthcare buildings based on climatological variables using PCA and linear regression
A1 Pérez Montalvo, Ernesto
A1 Zapata Velásquez, Manuel Eduardo
A1 Benítez Vázquez, Laura María
A1 Cermeño González, Juan Manuel
A1 Alejandro Miranda, Jose
A1 Martínez Cabero, Miguel Ángel
A1 Puente Gil, Álvaro de la
A2 Ingenieria Electrica
K1 Ingenierías
K1 PCA
K1 Data mining
K1 Regression models
K1 Smart metering
K1 Building energy index
K1 3306 Ingeniería y Tecnología Eléctricas
AB [EN] At this time, due to the global pandemic that has occurred, public administrations want to optimize resources and reducegreenhouse gases with more interest than before. It is the case of the Energy Regional Entity of the Junta de Castilla y León(Spain) that pursues the optimization of the energy consumption in particular of healthcare sector buildings. For this purpose,this work focuses on estimating electricity consumption for each month, for which different scenarios will be generated and thecorresponding model is obtained for each scenario. This model has been developed considering the historical monthly data ofconsumption and climatic variables for the last 3 years. Electricity consumption in public sanitary buildings is related to theirclimatology, due to the use of air conditioning to adjust the indoor temperature. Subsequently, from the models obtained, theresults will be analyzed. Significant differences have been observed in the estimation of electricity consumption with respectto the real data provided by the Junta de Castilla y León. The results obtained show how the availability of climatic variablesincreases the accuracy of the model obtained by about 30%.
PB Elsevier
LK https://hdl.handle.net/10612/17838
UL https://hdl.handle.net/10612/17838
NO Pérez-Montalvo, E., Zapata-Velásquez, M.-E., Benítez-Vázquez, L.-M., Cermeño-González, J.-M., Alejandro-Miranda, J., Martínez-Cabero, M.-Á., & de la Puente-Gil, Á. (2022). Model of monthly electricity consumption of healthcare buildings based on climatological variables using PCA and linear regression. Energy Reports, 8, 250-258. https://doi.org/10.1016/J.EGYR.2022.06.117
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 15-jun-2024