A review: machine learning for combinatorial optimization problems in energy areas
Combinatorial optimization problems (COPs) are a class of NP-hard problems with great
practical significance. Traditional approaches for COPs suffer from high computational time …
practical significance. Traditional approaches for COPs suffer from high computational time …
Frequency regulation capacity offering of district cooling system: An intrinsic-motivated reinforcement learning method
District cooling system (DCS), a type of large-capacity air conditioning system that supplies
cooling for multiple buildings, is an ideal resource to provide frequency regulation services …
cooling for multiple buildings, is an ideal resource to provide frequency regulation services …
Deep reinforcement learning in smart grid: Progress and prospects
The combination of reinforcement learning and deep learning has shown some remarkable
results in many scientific fields. Deep reinforcement learning algorithms are particularly …
results in many scientific fields. Deep reinforcement learning algorithms are particularly …
Reinforcement Learning for Intelligent Building Energy Management System Control
O Kotevska, P Andelfinger - … Data Mining and Analysis in Power …, 2022 - Wiley Online Library
A building energy management system (BEMS) is a computer‐based system designed to
monitor and control a building's energy needs. Modern BEMS rely on the sensing and …
monitor and control a building's energy needs. Modern BEMS rely on the sensing and …
Deep learning applications over heterogeneous networks: from multimedia to genes
JF CEVALLOS MOREN - 2022 - iris.uniroma1.it
This research aimed to investigate the synergies between deep learning and
heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the …
heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the …
Network Health: Intelligent MVNO Outages Understanding, Detection and Self-Healing
GJS Cruz - 2022 - estudogeral.uc.pt
In a world where fast and reliable connectivity is ever more a necessity rather than a luxury
and where networks grow both in user count and complexity, both architectural and service …
and where networks grow both in user count and complexity, both architectural and service …
Taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms
Abstract The future Internet of Things will have profound economic, commercial and societal
implications. IoT nodes are often resource restricted, making them attractive targets for cyber …
implications. IoT nodes are often resource restricted, making them attractive targets for cyber …
[PDF][PDF] GAUSSIAN PROCESSES FOR ONLINE LEARNING-BASED MODEL PREDICTIVE CONTROL
S GHOSH - 2022 - iitd.ac.in
Optimal control is a well established field that focuses on finding a control law that minimizes
a certain performance measure. This kind of control methodology is also known as model …
a certain performance measure. This kind of control methodology is also known as model …