LAPSE:2023.30309
Published Article
LAPSE:2023.30309
Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023
Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS−ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.
Keywords
Artificial Intelligence, Big Data, building energy, cooling load, deep learning, energy-efficiency, HVAC, Machine Learning, nature-inspired metaheuristic, smart buildings, smart city, zero energy
Suggested Citation
Moayedi H, Mosavi A. Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings. (2023). LAPSE:2023.30309
Author Affiliations
Moayedi H: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam
Mosavi A: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany; Institute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, Germany; John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary [ORCID]
Journal Name
Energies
Volume
14
Issue
6
First Page
1649
Year
2021
Publication Date
2021-03-16
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14061649, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.30309
This Record
External Link

doi:10.3390/en14061649
Publisher Version
Download
Files
[Download 1v1.pdf] (18.6 MB)
Apr 14, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
131
Version History
[v1] (Original Submission)
Apr 14, 2023
 
Verified by curator on
Apr 14, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.30309
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version