arrow
Return

Improved normal-boundary intersection algorithm: A method for energy optimization strategy in smart buildings

delete2022-03-01
delete8
delete
OA
AI
J
Jia Cui *
J
Jiang Pan
S
Shunjiang Wang *
M
Martin Onyeka Okoye
J
Junyou Yang
李阳 cover
李阳 (Yang Li) *
H
Hao Wang
DOI:10.1016/j.buildenv.2022.108846delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious. Subsequently, its energy optimal scheduling and multi-objective optimization have become more and more complex and need to be solved urgently. This paper presents a novel method to optimize energy utilization in smart buildings. Firstly, multiple transfer-retention ratio (TRR) parameters are added to the evaluation of distributed renewable energy. Secondly, the normal-boundary intersection (NBI) algorithm is improved by the adaptive weight sum, the adjust uniform axes method, and Mahalanobis distance to form the improved normal-boundary intersection (INBI) algorithm. The multi-objective optimization problem in smart buildings is solved by the parameter TRR and INBI algorithm to improve the regulation efficiency. In response to the needs of decision-makers with evaluation indicators, the average deviation is reduced by 60% compared with the previous case. Numerical examples show that the proposed method is superior to the existing technologies in terms of three optimization objectives. The objectives include 8.2% reduction in equipment costs, 7.6% reduction in power supply costs, and 1.6% improvement in occupants' comfort.
Keywords:
Smart building
Renewable energy dispatch
Multi-objective optimization
Normal-boundary intersection algorithm
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Building and Environment cover
Building and Environment
IF:
7.6
Papers:
1.3W
Citations:
6.6W

Organization

N
northeast electric power university
Scholars:
5.8K
Papers: 3.3K
Citations: 1
S
Shenyang University of Technology
Scholars:
5.0K
Papers: 3.3K
Citations: 3.4K