A survey on evolutionary neural architecture search
Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …
architectures of DNNs play a crucial role in their performance, which is usually manually …
Evolutionary machine learning: A survey
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
Electrical load forecasting using edge computing and federated learning
A Taïk, S Cherkaoui - ICC 2020-2020 IEEE international …, 2020 - ieeexplore.ieee.org
In the smart grid, huge amounts of consumption data are used to train deep learning models
for applications such as load monitoring and demand response. However, these …
for applications such as load monitoring and demand response. However, these …
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network
Remaining useful life (RUL) prediction is very important for improving the availability of a
system and reducing its life cycle cost. This paper proposes a deep long short-term memory …
system and reducing its life cycle cost. This paper proposes a deep long short-term memory …
[HTML][HTML] Systematic review of deep learning and machine learning for building energy
S Ardabili, L Abdolalizadeh, C Mako, B Torok… - Frontiers in Energy …, 2022 - frontiersin.org
The building energy (BE) sector has a vital role in urban sustainability. This sector contains
the two most important subjects including energy consumption and energy demand which …
the two most important subjects including energy consumption and energy demand which …
DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems
In the era of cutting edge technology, excessive demand for electricity is rising day by day,
due to the exponential growth of population, electricity reliant vehicles, and home …
due to the exponential growth of population, electricity reliant vehicles, and home …
Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting
Due to global warming and climate changes, buildings including residential and commercial
are significant contributors to energy consumption. To this end, net zero energy building …
are significant contributors to energy consumption. To this end, net zero energy building …
Data mining in the construction industry: Present status, opportunities, and future trends
H Yan, N Yang, Y Peng, Y Ren - Automation in Construction, 2020 - Elsevier
The construction industry is experiencing remarkable growth in the data generation. Data
mining (DM) from considerable amount of data in the construction industry has emerged as …
mining (DM) from considerable amount of data in the construction industry has emerged as …
An efficient deep learning framework for intelligent energy management in IoT networks
T Han, K Muhammad, T Hussain… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Green energy management is an economical solution for better energy usage, but the
employed literature lacks focusing on the potentials of edge intelligence in controllable …
employed literature lacks focusing on the potentials of edge intelligence in controllable …
Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks
Excessive Power Consumption (PC) and demand for power is increasing on a daily basis,
due to advancements in technology, the rise in electricity-dependent machinery, and the …
due to advancements in technology, the rise in electricity-dependent machinery, and the …