22,164 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
CaloriNet: From silhouettes to calorie estimation in private environments
We propose a novel deep fusion architecture, CaloriNet, for the online
estimation of energy expenditure for free living monitoring in private
environments, where RGB data is discarded and replaced by silhouettes. Our
fused convolutional neural network architecture is trainable end-to-end, to
estimate calorie expenditure, using temporal foreground silhouettes alongside
accelerometer data. The network is trained and cross-validated on a publicly
available dataset, SPHERE_RGBD + Inertial_calorie. Results show
state-of-the-art minimum error on the estimation of energy expenditure
(calories per minute), outperforming alternative, standard and single-modal
techniques.Comment: 11 pages, 7 figure
- โฆ