Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings

Autor: José Luis Castillo-Sequera, José Manuel Gómez-Pulido, Luis Mendoza-Pittí, Clara Simon de Blas, Miguel Vargas-Lombardo, Huriviades Calderón-Gómez
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Technology
Service (systems architecture)
Mean squared error
QH301-705.5
Computer science
QC1-999
020209 energy
daily energy consumption
02 engineering and technology
020401 chemical engineering
HVAC
short-term forecast
0202 electrical engineering
electronic engineering
information engineering

General Materials Science
Biology (General)
0204 chemical engineering
QD1-999
Instrumentation
Fluid Flow and Transfer Processes
business.industry
Physics
Process Chemistry and Technology
Deep learning
Work (physics)
General Engineering
deep learning
Energy consumption
Engineering (General). Civil engineering (General)
Computer Science Applications
Reliability engineering
Chemistry
forecasting model
Air conditioning
Artificial intelligence
TA1-2040
business
long short-term memory
HVAC systems
Efficient energy use
Zdroj: Applied Sciences
Volume 11
Issue 15
Applied Sciences, Vol 11, Iss 6722, p 6722 (2021)
ISSN: 2076-3417
DOI: 10.3390/app11156722
Popis: 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.
Databáze: OpenAIRE