Duque Perez, OscarZorita Lamadrid, Ángel LuisSolís, MartínGonzalez Morales, Luis GerardoHernández Callejo, LuisSantos García, FelixMariano Hernández, Deyslen2022-01-282022-01-2820212076-3417http://dspace.ucuenca.edu.ec/handle/123456789/37881https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114110387&doi=10.3390%2fapp11177886&partnerID=40&md5=04c6c1521938105f1a7ceab00ba6e9a7Smart buildings seek to have a balance between energy consumption and occupant com-fort. To make this possible, smart buildings need to be able to foresee sudden changes in the build-ing’s energy consumption. With the help of forecasting models, building energy management sys-tems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerlandes-ESMulti-step forecastingShort-term forecastingSmart buildingEnergy consumptionForecasting modelsARTÍCULO10.3390/app11177886