sensores para la detección de ocupación en una oficina universitaria Assessment of the Sensor-fusion Technique for Occupancy Detection in a University Office

Since the success of developing more energy efficie nt buildings has a surgical dependence on the occup ants' behavior and systems usage, the necessity of accounting for thei r correct behavior in the simulation of the early-s tage buildings' design and energy consumption evaluation, is increasing rapidly. To board this ne cessity, a technique called sensor-fusion is being widely implemented in the building domain, to first develop accurate descriptive models for occup ancy profiles, and ultimately, to be able to predic t typical profiles. In this context, an office is instrumented to monitor the indoor air quality, the power consumption, and the use of the window, and air conditioning unit. The real occupancy state was monitored manually. The data analysis all owed to highlight the most relevant parameters asso ciated with the occupancy state, based on the Spearman's correlation coefficient. The use of histograms allowed to identify an optimal sensor co mbination for detecting the occupancy state of the office room. The identified optimal combinat ion groups the CO2, power, and window state sensors, which detected t he occupancy with 91.5% of accuracy. Keywords– Occupancy, Sensor-fusion technique, Occupancy model ing, Office building, Histograms. 1. Introducción According to the international energy agency [1], i n 2017, buildings and appliances were responsible for around 30% of global energy use. Building energy us e increased by 0.8% from 2016 and rose 20% between 2000 and 2017. Since the success of developing more energy, efficient buildings and equipment have a su rgical dependence on the occupant ́s behavior and systems usage. Now with the implementation of new technologies oriented to energy saving and green building certifications, a new approach related to how they affect the use of energy due to occupant behavior has emer g d [2]. Include the occupant behavior is fundamental to quantifying the savings in energy consumption. For commercial buildings, behavioral change can reduce energy use by 5% to 30% [3]. Occupant movements and presence are fundamental to occupant behavior simulation by providing information about whether a room is occupied, the number of occupants, or the specific individual in the room, depending on the sensor or combination of sensors. The real occupancy patterns in buildings may differ significantly from each other. Existing research in cludes various data collection approaches, including noninvasive occupant observations, observing occupants tha have had perturbations applied, surveys, and labora tory studies [4]. Gathering data on human-building inter action is a new approach for achieving energy efficiency i n the building sector, and the research groups are focusi ng into evaluating the impact of occupant behavior in the building design and operation as a new horizon [5]. Measurements of energy-related behavior are collected using a) physical sensing, and b) non-phy sical sensing methods [6]. Data gathering to investigate occupant behavior studies in thermal comfort, occupancy, windows, shades and blinds, and lighting and electrical equipment were categorized by [7]. The i ndoor and outdoor environmental data are in the physical sensing category, and the authors identified the mo st significant as air temperature, air humidity, CO 2, occupancy, window state, door state, and others. Th e authors in [8] monitored some environmental paramet ers as CO2, carbon monoxide (CO), total volatile organic compounds (TVOC), outside temperature, dew point an d small particulates (PM 2.5), air temperature, relat ive humidity, motion detection, and acoustic to determi ning which parameters have significant correlation with the occupancy level. The results show the most signific ant correlation for CO2 and acoustic parameters with the number of occupants in the space. Sensor fusion approaches are built upon the use of multiple sensors or sensing modalities in an attemp to combine their advantages while canceling out their disadvantages as much as possible [9], [10]. In gen eral, to overcome the disadvantage of an individual detec tion system, a fusion of multiple sensors is encouraged in occupancy detection. 2. Experimental setup and monitoring parameters The experimental apparatus was designed to monitor the occupancy in an office at the University of Cal abria (Italy), as well as collecting data from the indoor environment such as air quality, occupancy state an d behavior, and electricity usage; this data collecti on started in February 2016 is ongoing. 2.1 Description of the office room Within the University building, submitted to Mediterranean climatic conditions, the instrumented office room has a surface area of 19 m 2 and 2.50 m height. This office has a Westward-facing external w l and a two-wing window of 0.68 x 0.76 m 2 (Figure 1 (a)). The equipment within the room includes desktop computers and printers, where the heating and cooli ng system are autonomous (Figure 1 (b)). Normally, the office is generally used by one person, where the information about how the office is used, was colle cted by personal interview. Figure 1. The university office: (a) external wall, (b) sche matic with sensors location. The detected variables for data collection were divided into two groups, binary and continuous: 1) Binary variables: Occupancy state (vacant = 0, present = 1), Window state (closed = 0, open = 1), Air conditioning usage (off = 0, on = 1). 2) Continuous variables: CO 2 (ppm), Power (W), VOC (ppm), Indoor air temperature (°C), Indoor air relative humidity (%). 2.2 Equipment for internal variables measurement Sensors were installed to obtain information regarding user presence and absence intervals. Data were automatically queried every one-minute and stored i n central embedded MySQL database. The experimental apparatus was designed to monitor the presence and movement of the occupants in the office, as well as thermophysical properties of the internal environment and the electricity consumptio n connected to the use of computers [11]. The sensor's location was carefully selected to ens ur that the sensors were triggered when occupants are insid the office. The indoor environment sensors were pla ced on internal walls at the height of 1.8 m above the floor, as suggested in [12] to avoid any perturbation from direct sunlight. The WuTility Version 4.30 tool was used for management and inventorying [13]. Indoor VOC and CO2 sensors for concentration levels and temperature/humidity sensors were the first install ed. The results were checked, and clock synchronization was set up. Preliminary tests for window/door position and air conditioning usage sensors were successively conduc ted before actual data collection. The CO2 sensor was installed near the desk at nose level (when sitting, 1.1 m above the ground) [8], a t this position more stable CO 2 values are ensured during the unoccupied periods. Table I shows the characteristi cs of the sensors used in the experimental apparatus. 3. Experimental results and data analysis Fig. 2 and Fig. 3 show hourly data of the occupancy state in relation to CO 2, VOC, air temperature, relative humidity, window state, power, and air conditioning usage for two summer days (1 st – 2 June. As shown in the graphs, just after the first person arrives in the office, all sensors indicate a change in their measurements . In the first day, the CO2 sensor (figure 2 (a)) seems to have a morning maximum reading around 11:00 and window closed. From about 11:30 to 14:30 (when the room is not occupied), all sensors register a decrease in their readings, except for the temperature. When the office is left vacant after 18:00, all sen sors measurements drop except for the humidity. Clearly, this behavior is strongly related to the occupancy state and also to the number of people that might have been i n the room at the moments were CO 2 levels reached high values even though windows remained open (Fig. 2 (a ) with figure 3 (a), for the second day at 12:00). Moreover, there appears to be a stable value for CO 2 concentration level and the power, when there is no occupancy. The same consideration can not be said f or indoor air temperature, relative humidity, and VOC. Table 1. Characteristics of sensors employed for data