Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings
Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/15/6722 |
| _version_ | 1797525856743063552 |
|---|---|
| author | Luis Mendoza-Pittí Huriviades Calderón-Gómez José Manuel Gómez-Pulido Miguel Vargas-Lombardo José Luis Castillo-Sequera Clara Simon de Blas |
| author_facet | Luis Mendoza-Pittí Huriviades Calderón-Gómez José Manuel Gómez-Pulido Miguel Vargas-Lombardo José Luis Castillo-Sequera Clara Simon de Blas |
| author_sort | Luis Mendoza-Pittí |
| collection | DOAJ |
| description | Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain. |
| first_indexed | 2024-03-10T09:19:56Z |
| format | Article |
| id | doaj.art-8b28c65f554741b38dc63415956b2502 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:19:56Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8b28c65f554741b38dc63415956b25022023-11-22T05:18:29ZengMDPI AGApplied Sciences2076-34172021-07-011115672210.3390/app11156722Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in BuildingsLuis Mendoza-Pittí0Huriviades Calderón-Gómez1José Manuel Gómez-Pulido2Miguel Vargas-Lombardo3José Luis Castillo-Sequera4Clara Simon de Blas5Department of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainDepartment of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainDepartment of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainSNI, Senacyt Panama, E-Health and Supercomputing Research Group (GISES), Technological University of Panama, Panama City 0819-07289, PanamaDepartment of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainDepartment of Computer’s Sciences, Technical School of Computer Engineering, Rey Juan Carlos University, 28933 Madrid, SpainForecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.https://www.mdpi.com/2076-3417/11/15/6722daily energy consumptiondeep learningforecasting modelHVAC systemslong short-term memoryshort-term forecast |
| spellingShingle | Luis Mendoza-Pittí Huriviades Calderón-Gómez José Manuel Gómez-Pulido Miguel Vargas-Lombardo José Luis Castillo-Sequera Clara Simon de Blas Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings Applied Sciences daily energy consumption deep learning forecasting model HVAC systems long short-term memory short-term forecast |
| title | Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings |
| title_full | Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings |
| title_fullStr | Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings |
| title_full_unstemmed | Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings |
| title_short | Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings |
| title_sort | developing a long short term memory based model for forecasting the daily energy consumption of heating ventilation and air conditioning systems in buildings |
| topic | daily energy consumption deep learning forecasting model HVAC systems long short-term memory short-term forecast |
| url | https://www.mdpi.com/2076-3417/11/15/6722 |
| work_keys_str_mv | AT luismendozapitti developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings AT huriviadescalderongomez developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings AT josemanuelgomezpulido developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings AT miguelvargaslombardo developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings AT joseluiscastillosequera developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings AT clarasimondeblas developingalongshorttermmemorybasedmodelforforecastingthedailyenergyconsumptionofheatingventilationandairconditioningsystemsinbuildings |