eprintid: 4052 rev_number: 10 eprint_status: archive userid: 2 dir: disk0/00/00/40/52 datestamp: 2022-10-17 23:30:04 lastmod: 2023-07-11 23:30:10 status_changed: 2022-10-17 23:30:04 type: article metadata_visibility: show creators_name: Chaganti, Rajasekhar creators_name: Rustam, Furqan creators_name: Daghriri, Talal creators_name: Díez, Isabel de la Torre creators_name: Vidal Mazón, Juan Luis creators_name: Rodríguez Velasco, Carmen Lilí creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: juanluis.vidal@uneatlantico.es creators_id: carmen.rodriguez@uneatlantico.es creators_id: title: Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: energy consumption prediction; cooling load; smart homes; Internet of Things; sustainable homes abstract: 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 date: 2022-10 publication: Sensors volume: 22 number: 19 pagerange: 7692 id_number: doi:10.3390/s22197692 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s22197692 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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 metadata Chaganti, Rajasekhar; Rustam, Furqan; Daghriri, Talal; Díez, Isabel de la Torre; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model. Sensors, 22 (19). p. 7692. ISSN 1424-8220 document_url: http://repositorio.unib.org/id/eprint/4052/1/sensors-22-07692-v2.pdf