RT info:eu-repo/semantics/article
T1 Building heating and cooling load prediction using ensemble machine learning model
A1 Chaganti, Rajasekhar
A1 Rustam, Furqan
A1 Daghriri, Talal
A1 Torre Díez, Isabel de la
A1 Vidal Mazón, Juan Luis
A1 Rodríguez, Carmen Lili
A1 Ashraf, Imran
K1 Energía - Consumo
K1 Aprendizaje automático
K1 Cooling systems
K1 Heating
K1 Calefacción
K1 Edificios sostenibles
K1 Edificios inteligentes
K1 Domotica
K1 3305.14 Viviendas
K1 3311.01 Tecnología de la Automatización
AB Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.
PB MDPI
SN 1424-8220
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61544
UL https://uvadoc.uva.es/handle/10324/61544
LA eng
NO Sensors, 2022, Vol. 22, Nº. 19, 7692
NO Producción Científica
DS UVaDOC
RD 11-jul-2024