Abstract
The present work aims to improve the comfort of architectural interior design and reduce indoor energy consumption. The Weight K-Nearest Neighborhood (WKNN) algorithm and Nondominated Sorting Genetic algorithm are proposed to locate and analyze the spatial location of indoor personnel and optimize the indoor energy consumption in combination with residential behavior. Firstly, the indoor human behavior data and energy-saving problems are analyzed based on residential behavior theory and architectural physics. The indoor positioning algorithm is proposed to identify the personnel activities to realize the optimization of indoor energy distribution. Secondly, mean filtering and cluster analysis are adopted to optimize sampling points' data and fingerprint database to eliminate data noise. Besides, the WKNN algorithm is used for Wireless Fidelity (Wi-Fi) indoor location fingerprint location. Then, aiming at the multiobjective optimization problem of building indoor energy consumption, the Nondominated Sorting Genetic algorithm obtains the optimal solution of the model. Combined with the indoor location information of personnel, the indoor heating and cooling system is monitored and distributed to reduce the energy consumption in the building while ensuring the living comfort of personnel. The test and simulation results demonstrate that the mean filtering algorithm can solve the room's fluctuation problem of Wi-Fi signals. The cluster analysis algorithm can eliminate the data noise of the fingerprint database and improve the positioning accuracy of the positioning algorithm. Moreover, the location result is independent of the number of nodes; the number of sampling points does not affect the location result. The positioning accuracy of the WKNN algorithm is 2 m, and the positioning error rate within 2 m is about 60%. Compared with other algorithms, the WKNN algorithm has better positioning accuracy and positioning stability. Therefore, the location algorithm designed here can be applied to indoor location optimization. This study provides a reference for optimizing buildings' indoor positioning and energy consumption.
Highlights
Residential behavior affects the interior design of buildings and the energy consumption of structures [1, 2]
A considerable number of domestic and foreign scholars have engaged in indoor positioning and building energy consumption optimization
Wireless Fidelity (Wi-Fi) Location Fingerprint Locating Algorithm Based on Weight K-Nearest Neighborhood (WKNN). e location fingerprint locating algorithm based on Wi-Fi is characterized by low cost, easy implementation, and good location effect, so it has gradually become the mainstream technology of indoor location research. e WiFi fingerprint locating algorithm uses the signal strength in the stored fingerprint database to match the signal strength of the currently connected beaker for location
Summary
With the improvement of the international economic level, consumers’ requirements for the interior design of buildings are constantly improving. With the rapid development of information technology, mobile phones, computers, and other information equipment are gradually changing people’s living style and potential energy behavior mode, putting forward new requirements for architectural interior design [3]. E large-scale collection of regular residential behavior information of residents in buildings can analyze the design innovation of residential buildings and the utilization of space in buildings in more detail [4] On this basis, the energy consumption in the building can be optimized to ensure the comfort of the indoor environment and reduce the energy consumption of the indoor environment [5].
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