Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of m...
Guardado en:
Autores principales: | Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, Manal Alghieth, Francisco Chiclana |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/586fd87ad23a4ca8987dd94f3f479249 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Energy Savings of Simultaneous Heating and Cooling System According to Indoor Set Temperature Changes in the Comfort Range
por: Dae-Uk Shin, et al.
Publicado: (2021) -
Evaluation of Heating and Cooling Loads for a Well-Insulated Single-Family House under Variable Climate Pattern
por: Prozuments Aleksejs, et al.
Publicado: (2021) -
Long-Term Policy Recommendations for Improving the Efficiency of Heating and Cooling
por: Laktuka Krista, et al.
Publicado: (2021) -
Electricity Theft Detection in Power Consumption Data Based on Adaptive Tuning Recurrent Neural Network
por: Guoying Lin, et al.
Publicado: (2021) -
Analysis of the Heat Pump Market in Europe with a Special Regard to France, Spain, Poland and Lithuania
por: Witkowska Agata, et al.
Publicado: (2021)