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

arXiv: https://arxiv.org/abs/2201.10303 · HTML: https://mlsanity.com/paper/2201.10303/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 2 of 2)

- [A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning](https://mlsanity.com/paper/2203.07075/) (2022)
- [Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks](https://mlsanity.com/paper/2205.11743/) (2022)

## Abstract (arXiv)

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.