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

TL;DR

This paper introduces an improved multi-objective optimization method for energy management in smart buildings, combining enhanced evaluation parameters and an advanced NBI algorithm to achieve significant cost reductions and comfort improvements.

Contribution

It proposes a novel INBI algorithm with adaptive weighting and Mahalanobis distance, and incorporates TRR parameters for better energy optimization in smart buildings.

Findings

60% reduction in average deviation of evaluation indicators

8.2% decrease in equipment costs

7.6% decrease in power supply costs

Abstract

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.