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 |
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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 |
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