Related Experiment Video
Updated: Oct 2, 2025
11:53
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
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Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building
Xiwen Yu1, Shaoxuan Wang2, Feng Xiao3
1School of Arts and Media, Hefei Normal University, Hefei, Anhui 230601, China.
Computational Intelligence and Neuroscience
|February 28, 2022
Summary
This study introduces algorithms for precise indoor positioning and energy optimization in buildings. The Weight K-Nearest Neighborhood (WKNN) and Nondominated Sorting Genetic algorithms enhance comfort and reduce energy use by analyzing human behavior and optimizing systems.
Area of Science:
- Architectural Physics
- Human-Building Interaction
- Computational Intelligence
Background:
- Improving indoor comfort and reducing building energy consumption are critical challenges in sustainable architectural design.
- Understanding and optimizing indoor environments requires accurate analysis of human presence and behavior.
Purpose of the Study:
- To develop an integrated system for indoor positioning and energy consumption optimization.
- To enhance architectural interior design for improved comfort and reduced energy usage.
- To analyze spatial location of indoor personnel and optimize energy distribution based on behavior.
Main Methods:
- Utilized the Weight K-Nearest Neighborhood (WKNN) algorithm for Wireless Fidelity (Wi-Fi) indoor location fingerprinting.
- Employed Nondominated Sorting Genetic algorithm for multiobjective optimization of building energy consumption.
- Applied mean filtering and cluster analysis to refine data and enhance fingerprint database accuracy.
Main Results:
- The WKNN algorithm achieved a positioning accuracy of 2 meters with a positioning error rate of approximately 60% within 2 meters.
- Mean filtering effectively addressed Wi-Fi signal fluctuations, while cluster analysis reduced data noise.
- The proposed algorithms demonstrated superior positioning accuracy and stability compared to existing methods.
Conclusions:
- The developed indoor positioning algorithm is effective for optimizing indoor location and energy consumption.
- This research offers a valuable reference for enhancing indoor positioning accuracy and energy efficiency in buildings.
- The integration of human behavior analysis with positioning and energy optimization significantly contributes to sustainable building design.
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