Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

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...

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Autores principales: Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, Manal Alghieth, Francisco Chiclana
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
building characteristics
deep neural networks
hyper-parameter tuning
prediction models
energy consumption
heating and cooling loads
Environmental effects of industries and plants
TD194-195
Renewable energy sources
TJ807-830
Environmental sciences
GE1-350
Acceso en línea:https://doaj.org/article/586fd87ad23a4ca8987dd94f3f479249
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https://doaj.org/article/586fd87ad23a4ca8987dd94f3f479249

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