Reduce the Number of Sensors

Abstract

As a consequence of rising energy prices, manifold solutions to create user awareness for the unnecessary operation of electric appliances have emerged, e.g., real-time consumption displays or timer-based switchable wall outlets. A common attribute of these solutions is the need to buy and install additional hardware, although their acquisition costs often diminish the attainable savings. Furthermore these solutions only permit to retrieve accumulated figures of the energy consumption. Especially in households or office spaces with multiple persons, however, attributing electricity consumption to individuals provides enormous potential to determine possible savings. We therefore propose a system that allows to identify the energy demand incurred by a user's action based on audio recordings using smartphones. More precisely, we capture the user's ambient sounds and applying suitable filtering steps in order to determine the user's current activity. Our results indicate that our system is capable of detecting 16 typical household activities at an accuracy of 92%. By annotating the detectable household activities with information about typical energy consumptions, extracted from 950 real-world power consumption traces, a good estimate of the energy intensity of the users' lifestyles can be made. Our novel personalized energy monitoring system shows people their personal energy consumption, while maintaining their user comfort and relinquishing the need for additional hardware.

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

“…Gaussian and agent-based prediction models have been proposed for the prediction of occupant mobility patterns and prediction of room usage for energy-efficient buildings. 38 Englert et al 127 presented a system SensiMate which learned the energy consumption pattern based on occupant activity and suggested possible energy savings. A two-tier classifier system was introduced by Srinivasan and Phan 88 to reduce the wake time of the activity recognition system, thus reducing the power consumption of sensing devices.…”

“…This can further be used to identify the energy demand of the user without any additional sensing infrastructure. 92 Tung and Shin 59 presented an approach Ecotag to detect occupancy. In this, the acoustic signal was transmitted from a mobile phone and the reflected signal was received by the phone’s microphone, allowing the device to learn its location as well as detect the occupancy around it.…”