Machine learning based multi-physical-model blending for enhancing renewable energy forecast - improvement via situation dependent error correction

2015 ◽  
Author(s):  
Siyuan Lu ◽  
Youngdeok Hwang ◽  
Ildar Khabibrakhmanov ◽  
Fernando J. Marianno ◽  
Xiaoyan Shao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Error Correction ◽  
Physical Model ◽  
Energy Forecast ◽  
Dependent Error

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

2021 ◽  
Author(s):  
Matteo Ridolfi ◽  
Jaron Fontaine ◽  
Ben Van Herbruggen ◽  
Wout Joseph ◽  
Jeroen Hoebeke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Self Calibration ◽  
Anchor Nodes

scholarly journals PyADM1: a Python implementation of Anaerobic Digestion Model No. 1

2021 ◽  
Author(s):  
Peyman Sadrimajd ◽  
Patrick Mannion ◽  
Enda Howley ◽  
Piet N. L. Lens
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Mathematical Model ◽  
Renewable Energy ◽  
Anaerobic Digestion ◽  
Waste Treatment ◽  
Treatment Technology ◽  
Software Implementations ◽  
Solid Waste Treatment ◽  
Direct Use

Anaerobic Digestion (AD) is a waste treatment technology widely used for wastewater and solid waste treatment, with the advantage of being a source of renewable energy in the form of biogas. Anaerobic digestion model number 1 (ADM1) is the most common mathematical model available for AD modelling. Commercial software implementations of ADM1 are available but have limited flexibility and availability due to the closed sources and licensing fees. Python is the fastest growing programming language and is open source freely available. Python implementation of ADM1 makes this AD model available to the mass user base of the Python ecosystem and it [prime]s libraries. The open easy to use implementation in PyADM1 makes it more accessible and provides possibilities for flexible direct use of the model linked to other software, e.g. machine learning libraries or Linux operating system on embedded hardware.

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