Estimating Heating Load in Residential Buildings Using Multi-Verse Optimizer, Self-Organizing Self-Adaptive, and Vortex Search Neural-Evolutionary Techniques
Auteur(s): |
Fatemeh Nejati
Nayer Tahoori Mohammad Amin Sharifian Alireza Ghafari Moncef L. Nehdi |
---|---|
Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 16 septembre 2022, n. 9, v. 12 |
Page(s): | 1328 |
DOI: | 10.3390/buildings12091328 |
Abstrait: |
Using ANN algorithms to address optimization problems has substantially benefited recent research. This study assessed the heating load (HL) of residential buildings’ heating, ventilating, and air conditioning (HVAC) systems. Multi-layer perceptron (MLP) neural network is utilized in association with the MVO (multi-verse optimizer), VSA (vortex search algorithm), and SOSA (self-organizing self-adaptive) algorithms to solve the computational challenges compounded by the model’s complexity. In a dataset that includes independent factors like overall height and glazing area, orientation, wall area, compactness, and the distribution of glazing area, HL is a goal factor. It was revealed that metaheuristic ensembles based on the MVOMLP and VSAMLP metaheuristics had a solid ability to recognize non-linear relationships between these variables. In terms of performance, the MVO-MLP model was considered superior to the VSA-MLP and SOSA-MLP models. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
2.4 MB
- Informations
sur cette fiche - Reference-ID
10692790 - Publié(e) le:
23.09.2022 - Modifié(e) le:
10.11.2022