A review on deep learning techniques for IoT data
Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …
internet-based sensor tools that provide physical world observations and data …
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …
lives, including environmental pollution, public security, road congestion, etc. New …
Deep learning for IoT big data and streaming analytics: A survey
In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …
and/or generate various sensory data over time for a wide range of fields and applications …
On-line building energy optimization using deep reinforcement learning
Unprecedented high volumes of data are becoming available with the growth of the
advanced metering infrastructure. These are expected to benefit planning and operation of …
advanced metering infrastructure. These are expected to benefit planning and operation of …
Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services
Today, 44% of global energy has been derived from fossil fuel, which currently poses a
threat to inhabitants and well-being of the environment. In a recent investigation of the global …
threat to inhabitants and well-being of the environment. In a recent investigation of the global …
Deep neural networks for energy load forecasting
K Amarasinghe, DL Marino… - 2017 IEEE 26th …, 2017 - ieeexplore.ieee.org
Smartgrids of the future promise unprecedented flexibility in energy management. Therefore,
accurate predictions/forecasts of energy demands (loads) at individual site and aggregate …
accurate predictions/forecasts of energy demands (loads) at individual site and aggregate …
[HTML][HTML] Artificial intelligence for electricity supply chain automation
Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …
processes ranging from production to transportation and consumption of electricity. The …
[HTML][HTML] Powering nodes of wireless sensor networks with energy harvesters for intelligent buildings: A review
Intelligent buildings play a fundamental role in achieving efficient energy management in the
building sector in many countries worldwide. Improving energy consumption within a …
building sector in many countries worldwide. Improving energy consumption within a …
XNOR neural engine: A hardware accelerator IP for 21.6-fJ/op binary neural network inference
Binary neural networks (BNNs) are promising to deliver accuracy comparable to
conventional deep neural networks at a fraction of the cost in terms of memory and energy …
conventional deep neural networks at a fraction of the cost in terms of memory and energy …
Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities
This paper focuses on analytics of an extremely large dataset of smart grid electricity price
and load, which is difficult to process with conventional computational models. These data …
and load, which is difficult to process with conventional computational models. These data …