A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamen...
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doaj-f9f00172da8944c8809bf1da0492d93a2021-09-09T13:38:31ZengMDPI AGApplied Sciences2076-34172021-08-01117886788610.3390/app11177886A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart BuildingsDeyslen Mariano-Hernández0Luis Hernández-Callejo1Martín Solís2Angel Zorita-Lamadrid3Oscar Duque-Perez4Luis Gonzalez-Morales5Felix Santos-García6Área de Ingeniería, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican RepublicADIRE-ITAP, Departamento Ingeniería Agrícola y Forestal, Universidad de Valladolid, 47002 Valladolid, SpainTecnológico de Costa Rica, Cartago 30101, Costa RicaADIRE-ITAP, Departamento de Ingeniería Eléctrica, Universidad de Valladolid, 47002 Valladolid, SpainADIRE-ITAP, Departamento de Ingeniería Eléctrica, Universidad de Valladolid, 47002 Valladolid, SpainDepartamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones—DEET, Facultad de Ingeniería, Universidad de Cuenca, Cuenca 010107, EcuadorÁrea de Ciencias Básicas y Ambientales, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican RepublicSmart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, 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.https://www.mdpi.com/2076-3417/11/17/7886forecasting modelsenergy consumptionmulti-step forecastingshort-term forecastingsmart building |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Deyslen Mariano-Hernández Luis Hernández-Callejo Martín Solís Angel Zorita-Lamadrid Oscar Duque-Perez Luis Gonzalez-Morales Felix Santos-García |
spellingShingle |
Deyslen Mariano-Hernández Luis Hernández-Callejo Martín Solís Angel Zorita-Lamadrid Oscar Duque-Perez Luis Gonzalez-Morales Felix Santos-García A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings Applied Sciences forecasting models energy consumption multi-step forecasting short-term forecasting smart building |
author_facet |
Deyslen Mariano-Hernández Luis Hernández-Callejo Martín Solís Angel Zorita-Lamadrid Oscar Duque-Perez Luis Gonzalez-Morales Felix Santos-García |
author_sort |
Deyslen Mariano-Hernández |
title |
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings |
title_short |
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings |
title_full |
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings |
title_fullStr |
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings |
title_full_unstemmed |
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings |
title_sort |
data-driven forecasting strategy to predict continuous hourly energy demand in smart buildings |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-08-01 |
description |
Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, 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. |
topic |
forecasting models energy consumption multi-step forecasting short-term forecasting smart building |
url |
https://www.mdpi.com/2076-3417/11/17/7886 |
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