Fine-grained spatial utilization enhances post-occupancy evaluation (POE) precision. Traditional methods are limited by lower spatiotemporal resolution and smaller datasets, whereas indoor positioning systems offer high-precision occupancy data. The proposed indoor space utilization index combing spatial scale, occupancy points, and duration of stay to evaluate the distribution of spatio-temporal behavior and utilization rates within functional zones. Among a two-month period, a dataset of over 200,000 unlabeled behavioral data was collected in an open-office building using Wi-Fi and Bluetooth positioning systems. Through data processing and point projection, it is found that: (1) Point data shows spatio-temporal variations across floors, weekdays versus weekends, and different times of day. (2) High-density, long-duration workstation areas are highly utilized, while low-density, short-duration public spaces are underutilized. (3) Multi-functional atriums, open discussion areas, and entrance-linked elevators are most utilized, reflecting employee work patterns. Analysis of Kullback–Leibler divergence across different spatio-temporal units confirmed the reliability of conclusions, demonstrating that 20 weekdays of valid mobile phone data yield consistent results irrespective of grid sizes. This paradigm leverages long-term, non-intrusive, high-precision positioning data from Wi-Fi and Bluetooth systems to accurately track space utilization dynamics in real time across various scales, supporting human-centered POE.

Demand Response (DR) is a critical strategy for managing the integration of renewable energy sources into the power grid, addressing the challenges posed by their intermittent and unpredictable nature. This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating, Ventilation, and Air Conditioning (HVAC) systems, focusing on the significant role these systems play in energy consumption and grid flexibility. Firstly, the methodology involves constructing a simulation model library that encompasses three dimensions including room type, room location, and internal heat gain mode to reflect the dynamic characteristics of cooling load. Additionally, batch simulations generate DR profiles under various typical weather conditions, and surrogate models are trained for each simulation model, leveraging feature engineering and cross-validation to enhance accuracy. The Multi-Layer Perceptron (MLP) surrogate models achieve high accuracy in predicting DR potential under various scenarios, with R² values exceeding 0.95. This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems. It supports the quantification of response capabilities and facilitates participation in bidding processes. Furthermore, it highlights the potential of data-driven models to enable rapid and scalable energy management.

Indoor environment quality (IEQ) is one of the most concerned building performances during the operation stage. The non-uniform spatial distribution of various IEQ parameters in large-scale public buildings has been demonstrated to be an essential factor affecting occupant comfort and building energy consumption. Currently, IEQ sensors have been widely employed in buildings to monitor thermal, visual, acoustic and air quality. However, there is a lack of effective methods for exploring the typical spatial distribution of indoor environmental quality parameters, which is crucial for assessing and controlling non-uniform indoor environments. In this study, a novel clustering method for extracting IEQ spatial distribution patterns is proposed. Firstly, representation vectors reflecting IEQ distributions in the concerned space are generated based on the low-rank sparse representation. Secondly, a multi-step clustering method, which addressed the problems of the “curse of dimensionality”, is designed to obtain typical IEQ distribution patterns of the entire indoor space. The proposed method was applied to the analysis of indoor thermal environment in Beijing Daxing international airport terminal. As a result, four typical temperature spatial distribution patterns of the terminal were extracted from a four-month monitoring, which had been validated for their good representativeness. These typical patterns revealed typical environmental issues in the terminal, such as long-term localized overheating and temperature increases due to a sudden influx of people. The extracted typical IEQ spatial distribution patterns could assist building operators in effectively assessing the uneven distribution of IEQ space under current environmental conditions, facilitating targeted environmental improvements, optimization of thermal comfort levels, and application of energy-saving measures.