Cited by Intelligent Identification of Dangerous Behaviors in Power Production Based on Gaussian Convolutional Deep Belief Network

Study on the Application of Meteorological Data Based on K-Means Method to Highway Wind-Blown Sand Protection DOI

Yu Sun,

Yanbin Zhou,

Jiang Hua

et al.

Frontiers in artificial intelligence and applications, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 12, 2024

Highway wind-blown sand reduces vehicle visibility and is likely to cause rollover other accidents. In this paper, k-means algorithm used extract the annual maximum wind speed data of 10 stations in central western regions Inner Mongolia analyze different seasons. Through results, it can be clearly seen that all operate from April June October December. Wind shows strong characteristics. The performance also affected by topography Mongolia, which further increases unsafe factors road traffic. addition, results highway environment around meteorological station Wengeng Town Bayannur City more susceptible influence sand, sandstorms, accumulation phenomena. cluster analysis provide scientific support for maintenance department effectively resist disaster caused sand.

Language: Английский

Citations

The Scheduling Role of Future Pricing Information in Electricity Markets with Rising Deployments of Renewables and Energy Storage: An Australian National Electricity Market Case Study DOI

Abhijith Prakash, Anna Bruce, Iain MacGill

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Share: Permalink Using these links will ensure access to this page indefinitely Copy URL DOI

Language: Английский

Citations

Interpretable Artificial Intelligence Evolved Policies Applied in Renewable Energy Trading DOI

Konstantinos Parginos,

Ricardo J. Bessa, Simon Camal

et al.

Published: Jan. 1, 2024

—Trading energy generated by renewable source (RES) power plants on electricity markets raises a challenging decision-making issues due to uncertain prices and generation forecasts. In the past few decades, researchers industry have employed analytical artificial intelligence approaches address this issue. The main objective of work is build an inherently interpretable data-driven decision-aid model for RES short-term trading, with aim minimising imbalance costs (maximizing revenue), using symbolic expressions derived through Symbolic Learning (SL) based Genetic Programming. addition being human decision-maker, can be transferred generalized different policies trained solar wind replicated under market conditions. Since SL evolves combinations nonlinear functions input variables according humanspecified grammatical space, novel approach also has ability integrate expert knowledge into decision aid algorithm. proposed method was tested two open datasets from operational showed robust results in both case studies. improve substantially when included space.

Language: Английский

Citations

Logic-based explanations of imbalance price forecasts using boosted trees DOI

Jérémie Bottieau, Gilles Audemard,

Steve Bellart

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 235, P. 110699 - 110699

Published: June 29, 2024

Language: Английский

Citations

Intelligent Identification of Dangerous Behaviors in Power Production Based on Gaussian Convolutional Deep Belief Network DOI

Xin Wan,

Chunhua Tao,

Min Liu

et al.

Published: July 26, 2024

Language: Английский

Citations