Application of artificial neural network model for forecast energy efficiency of the cryogenic liquefaction system in the meaning of sustainability

ALTINTAŞ, Elif; Tolon, Mert; Karabuga, Arif; Utlu, Zafer

doi:10.1504/ijgw.2021.116718

Cited by 3 publications

(3 citation statements)

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“…The results confirm that this model has higher accuracy than other artificial neural networks and rough set models. Work by Altintas et al [50] is based on the principle of thermodynamics as an audit and forecasting tool and predicts the system's energy efficiency by applying different ANN architecture types. Rossi et al [51] combined machine learning algorithms with advanced statistical analysis to build a machine learning model based on gradient boosting regression algorithm to predict the factory's energy efficiency.…”

Section: Research On Energy Forecasting Methodsmentioning

confidence: 99%

Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream

Wang

2022

Sustainability

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As the fifth-longest river globally, the Yellow River is of great importance to the world’s ecological protection. Due to its location as an essential ecological barrier and economic zone, it is imperative to balance energy support and ecological management in the basin. In this process, improving energy efficiency is crucial solution. Distinguished into upstream, midstream, and downstream, we measured the trajectory of green total factor energy efficiency over the past fifteen years using the Super-Epsilon-based model. Further, we identified the heterogeneity of energy efficiency within different river basins with the help of kernel density estimation. We used it to analyze the geographical and policy reasons affecting energy efficiency fluctuations. Finally, we constructed high, medium, and low GDP growth scenarios, and used a long short-term memory neural network model to predict energy efficiency forecasts in each scenario. The study results clarified that the overall energy efficiency showed an upward trend since 2013. Among them, the most significant improvement in energy efficiency was observed upstream, while the energy efficiency in the middle and lower stream showed a decreasing trend. Regarding future development trends, an economic growth rate of 6.5% was most favorable for energy efficiency compared to 6% and 7%. This finding reminded us to be alert to the ecological condition of the lower Yellow River basin. In addition, maintaining an appropriate economic growth rate is helpful for the balance between development and ecology.

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Section: Research On Energy Forecasting Methodsmentioning

confidence: 99%

Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream

Wang

2022

Sustainability

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“…In recent years, with the development of artificial neural network theory and technology, its application in video compression has gradually attracted people's attention. Compared with some traditional compression methods, the artificial neural network technology [8,9] has good fault tolerance, self-organization and adaptability. Therefore, it is not necessary to use some predetermined data coding algorithm in the video compression process, but can independently complete the video compression completely according to the information characteristics of the video itself.…”

Section: A Video Compressionmentioning

confidence: 99%

Design of Personalized VR Short Video Content Distribution Algorithm based on Artificial Neural Network

Han¹,

Choi²,

Choi³

et al. 2022

IJACSA

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In order to improve the video quality, reduce the number of video jams, and improve the video transcoding rate, a new personalized distribution algorithm of VR short video content based on artificial neural network is proposed. BP neural network is used to compress the original video and determine the execution mode and cache location of VR short video cache file; Transcoding cached high bit rate VR short video stream to generate low bit rate video stream to meet the needs of different network bandwidth; Build a video distribution model, and build a multicast distribution tree based on this model, that is, add some relay servers to minimize the video distribution cost; Finally, through the algorithm of minimizing the distribution cost of VR short video, the bandwidth loss and response delay are effectively reduced to achieve the goal of minimizing the distribution cost. The experimental results show that the video blocking times of this method are always less than six times, which effectively reduces the video blocking times. The PSNR value is high, the increase is large, up to 0.5, and the video transcoding rate is improved, up to 92%.

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“…The second analysis includes passenger transfers between different services, discusses how synchronised timetables improve connectivity and points to the importance of such coordination. In literature [6], the simple model based on artificial neural network was proposed to be compared with the model based on thermodynamic principles as an audit and prediction tool. Different types of artificial neural network architecture were used to predict the measured data of 441 liquefied nitrogen experiments, and the engineering equation solver (EES) program was analyzed to predict the exergy efficiency of the system.…”

Section: Introductionmentioning

confidence: 99%

Automatic search model of railway shunting route based on improved artificial neural network algorithm

Niu¹

2023

Preprint

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In order to improve the accuracy of route search and reduce the memory occupancy, a design method of automatic route search model for railway shunting based on improved artificial neural network algorithm is proposed. Build the road network topology that changes with time in real time, analyze the train route selection process, and remove the schemes that cannot be realized due to route conflict from the feasible solution set. According to the train route selection process, the improved artificial neural network method is used to optimize the train route arrangement at the station. Make full use of the similarity between the station yard structure and the binary tree structure to build an automatic search model for railway shunting routes, transform the route search process into the traversal process of the binary tree, simplify the search process, and improve the work efficiency. Finally, based on ant colony algorithm, the automatic search method of railway shunting route is optimized to generate dynamic route table and reduce the memory occupancy. The experimental results show that the method in this paper has a high accuracy of route search and a low memory occupation rate, which effectively improves the automatic route search effect of railway shunting.

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Application of artificial neural network model for forecast energy efficiency of the cryogenic liquefaction system in the meaning of sustainability

Cited by 3 publications

References 0 publications

Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream

Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream

Design of Personalized VR Short Video Content Distribution Algorithm based on Artificial Neural Network

Automatic search model of railway shunting route based on improved artificial neural network algorithm

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