Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network. (8th March 2023)

• Record Type: Journal Article
• Authors: Ali Al‐Fadheeli, Mohammed Saud; Bouteraa, Yassine; Mohammed, Adil Hussein; Mahmood Salman, Hayder; Ghasemi, Ghadir
• Is Part Of: Concurrency and computation. Volume 35:Number 10(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-08
• Subjects: ASLOA‐ENN -- building energy utilization prediction -- bio‐inspired algorithms -- building simulation -- prediction; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7645
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: There are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over‐estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.