ThermoSense

Abstract

In order to achieve sustainability, steps must be taken to reduce energy consumption. In particular, heating, cooling, and ventilation systems, which account for 42% of the energy consumed by US buildings in 2010 [8], must be made more efficient. In this paper, we demonstrate ThermoSense, a new system for estimating occupancy. Using this system we are able to condition rooms based on usage. Rather than fully conditioning empty or partially filled spaces, we can control ventilation based on near real-time estimates of occupancy and temperature using conditioning schedules learned from occupant usage patterns. ThermoSense uses a novel multisensor node that utilizes a low-cost, low-power thermal sensor array along with a passive infrared sensor. By using a novel processing pipeline and sensor fusion, we show that our system is able measure occupancy with a RMSE of only ≈0.35 persons. By conditioning spaces based on occupancy, we show that we can save 25% energy annually while maintaining room temperature effectiveness.

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

“…Our approach to detect the human presence using Random Forest algorithm is described by the algorithm in Algorithm 1: We have considered as dataset for this phase the set with processed features obtained from the raw temperature values: active pixels, number of blobs and the size of the largest blob, as in [8]. After performing another step to find the importance of each feature in the classification process, Figure 9, we only used the number of active pixels and the number of blobs.…”

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

“…We have considered as dataset for this phase the set with processed features obtained from the raw temperature values: active pixels, number of blobs and the size of the largest blob, as in [8]. After performing another step to find the importance of each feature in the classification process, Figure 9, we only used the number of active pixels and the number of blobs.…”

Occupancy detection systems for indoor environments: A survey of approaches and methods

“…Prediction : Occupancy prediction is based on the behaviour and pattern of occupants. 36 Here, the sensory infrastructure observes the behaviour and pattern of the users and creates a prediction model using various probabilistic and machine learning techniques. The models only work when occupants follow a pre-defined behaviour; if the predicted data differ substantially from ground truth data at any time, then the prediction model updates itself using the current pattern.…”

“…Resource optimization : The prime application of an occupancy detection system is resource optimization, for example the optimization of energy consumption in buildings, 25–32 and the control of HVAC equipment, 33–43 lighting systems 44–46 and business equipment. 47–50…”