A hybrid model based on multivariate fast iterative filtering and long short-term memory for ultra-short-term cooling load prediction

Author:

Myat Aung,Kondath Namitha,Soh Yong Loke,Hui An

Funder

Government of Singapore Building and Construction Authority

National Research Foundation of Singapore

Publisher

Elsevier BV

Reference47 articles.

1. A study on energy performance of 30 commercial office buildings in Hong Kong;Jing;Energ. Buildings,2017

2. Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables;Fan;Energ. Buildings,2020

3. An improved attention-based deep learning approach for robust cooling load prediction: public building cases under diverse occupancy schedules;Lu;Sustain. Cities Soc.,2023

4. Assessment of CO2 emissions associated with HVAC system in buildings equipped with phase change materials;Farouk;J. Build. Eng.,2022

5. Research on short-term and ultra-short-term cooling load prediction models for office buildings;Ding;Energ. Buildings,2017

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