| Abstract | The energy use of many buildings is significantly influenced by the presence and number of occupants. The prevalence of personal mobile devices has highlighted Wi-Fi data as a strong indicator of occupancy levels, with a strong correlation between the number of occupants and the number of Wi-Fienabled devices in a building. With accurate occupancy-count estimations, occupancy-based controls for building HVAC systems have the potential to realize energy savings. However, reactive controls based on instantaneous occupancy-count estimates are not sufficient for optimal operation. Instead, characterizing occupancy patterns can allow building operators to make proactive and informed decisions about equipment schedules, which has the potential to reduce energy use. This paper presents a Wi-Fi based approach for occupancy pattern detection, using an academic office building as a case study. Occupancy in many buildings - including the case study building - is not entirely stochastic; a visual inspection of Wi-Fi time series data will reveal repetitious patterns throughout the year, such as several distinct weekday and weekend profiles, called motifs. Seven months of continuous Wi-Fi time series data is processed through an occupancy-count estimation function to develop predicted occupancy profiles for each day. These occupancy profiles are clustered using several different approaches, including hierarchical agglomerative clustering with different dissimilarity metrics and k-means clustering. The results and performance indices for different clustering techniques are discussed. Based on this analysis, typical cluster profiles are extracted. Each typical profile is quantized using alphabetic characters and a character is assigned to each day. These characters are combined into corresponding words for each week. Frequently repeated weekly words are identified, and rule extraction is performed using a classification tree to develop a day-ahead occupancy forecast. The results show that this methodology can be used on Wi-Fi data to generate insights into occupancy patterns in the case study building. The forecastingframework can also be used to accurately forecast occupancy over the 24-hour prediction horizon. Future work will implement a control scheme based on the results from this study in a real-world air handling unit to quantify energy savings. |
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