An occupancy-based strategy employing computer vision for reducing cooling energy consumed in buildings

The energy expended to cool the occupied areas by air conditioners represents a substantial share of the total energy exhausted in buildings. Therefore, developing strategies to reduce this energy is crucial. One of the preponderance strategies adopted to depreciate energy consumption in buildings is the occupancy-based strategy. In this research, an innovative model was established to achieve the goal of reducing cooling energy consumed in buildings based on occupancy-based combined with a constant temperature setpoint strategy in two phases, and each phase engrosses in 20 days. Phase one is to identify the extent of cooling energy employed according to the use of room occupants and its costs in consumption was 276.01 kWh after completion of this phase. Sequentially, constructing phase two intended to reduce cooling energy consumption by employing an automatic air-conditioner (AC) control strategy relying on an improved human detection algorithm with a 25℃ as temperature setpoint, resulting in 112.45 kWh of consumption. To complement the motives for elaboration, the human detection measurement using you only look once (YOLO) improved by applying pre-processing algorithms to reach an average human detection enhancement of 21.2%. The proposed model results showed that potential savings associated with the embraced strategy decreases by more than anticipated as the amount of reduced energy reached 59% savings.

Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception

Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.