Bangladesh is an agricultural-based country whoseeconomy depends upon its agriculture. However, ur-banization and industrialization have changed over recent years. Therefore, it is now experiencing an acuteelectric ...
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With the rapid growth of technologies, the Smart grid (SG) has attracted a lot of academic and commercial attention in recent years. Smart cities can achieve magnificent energy management through extensive monitoring ...
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Regional power grids with integration of wind and photovoltaic (PV) generation are highly sensitive to extreme meteorological events. In this paper, two data-driven schemes are proposed to model and simulate the power...
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ISBN:
(纸本)9798350375145;9798350375138
Regional power grids with integration of wind and photovoltaic (PV) generation are highly sensitive to extreme meteorological events. In this paper, two data-driven schemes are proposed to model and simulate the power demand sequences of loads as well as the power generation sequences of wind power and photovoltaic power plants during typhoon events by establishing a time-sequence model of loads based on the landfall time of the typhoon, and a time-sequence model of the new energy output based on the intensity of the typhoon and the path of the typhoon. On this basis, the operation simulation can be carried out to study the risk of high wind/PV penetration distribution system during typhoon event. The proposed scheme is tested in an 110kV regional network and a set of risk evaluation indices are calculation to present the impact of typhoon to the power supply reliability.
With the development of smart grid, the path planning of digital transmission lines has become an important research direction. The path planning of transmission line is to determine the direction of transmission line...
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Reservoir computing (RC) is a very lightweight machine learning framework, suitable for edge information processing in theera of IOT. Recently, increasing efforts on RC to be implemented using various novel materials...
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ISBN:
(纸本)9798350375220;9798350375213
Reservoir computing (RC) is a very lightweight machine learning framework, suitable for edge information processing in theera of IOT. Recently, increasing efforts on RC to be implemented using various novel materials and devices areemerging, such as carbon nanotube, memristor, etc. However, how to configure the reservoir's behavior suitable for a certain application is still an open problem. In this paper, our attention is paid to the behavior space of the reservoir including its all the currently available task-independent evaluation metrics, i.e., rank of kernel quality, rank of generalization capacity, information processing capacity, and memory capacity. A genetic algorithmis used to explore the space in order to identify the performance boundary of the reservoir. It is applied to characterize thecomputing capabilities of a carbon nanotube based in-materio reservoir system, eespecially to find its critical metric to implement the prediction of nonlinear series such as Nonlinear Autoregressive Moving Average with 10th order time-lag (NARMA 10) .
Due to the depletion of fossil fuels globally and the rapid advancement of solar power generating technologies, solar power generation has increasingly emerged as a viable method for energy development. The concept of...
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Intelligent Transportation Systems (ITS) have been identified as a key application of the Internet of Things (IoT). In order to reduce latency and bandwidth needs, edgecomputing offers a distributed computing archite...
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Patrol route optimization is not only related to flight safety and efficiency, but also directly related to many aspects such as resource utilization and environmental protection. However, some unreasonable route plan...
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V2G (Vehicle to grid) is a concept that aims to utilize the capacity of vehicle batteries when vehicles (primarily cars) are stationary. It effectively gives rise to a bi-directional flow of energy between vehicles an...
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The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one...
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