Universal workflow of artificial intelligence for energy saving

Lee, Dasheng; Yan-tang, Chen; Chao, Shiau-Fang

doi:10.1016/j.egyr.2021.12.066

Cited by 31 publications

(5 citation statements)

References 110 publications

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“…It is concluded that the use of artificial intelligence technology can improve energy efficiency and renewable energy use [ 57 ]. Furthermore, based on algorithms, Lee et al (2022) concluded that artificial intelligence can achieve energy savings in different fields [ 68 ]. In contrast, we are not an algorithm or forecasting study, but based on econometrics, using micro-data of manufacturing companies, we have proved the impact of AI on energy efficiency.…”

Section: Discussionmentioning

confidence: 99%

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Liu

et al. 2022

IJERPH

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Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.

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Section: Discussionmentioning

confidence: 99%

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Liu

et al. 2022

IJERPH

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“…It conducted tests and comparisons between surge arresters with various defects (such as aging due to solar radiation, aging of resistive valve elements, and contamination). Similarly, Lee et al [19] proposed a fuzzy environment-based MOA fault identification method. It locally fuzzified the surrounding environment affecting leakage current and used an adaptive particle swarm algorithm to obtain optimal values for the SVM penalty factor and kernel function parameters to achieve the best performance.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Road Network Speed around Sports Events Based on Random Forest Model

Qian

Wang

2021

Cictp 2021

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The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a comprehensive control system and effective control measures, it is not possible to trace the execution process of business operations afterward. This inability to trace hampers the investigation of testing issues and risk control, as it lacks effective data support. Additionally, a significant amount of original record information for key parameters in the testing process, which is based on sampling operation standards, has not been effectively utilized.To address these issues, we conduct researches on key monitoring technologies in the typical material inspection process based on the Internet of Things (IoT) and analyze the key parameters in inspection results. For purpose of complete the above tasks, this paper investigates the use of Long Short-Term Memory (LSTM) algorithms for quality prediction in material equipment based on key inspection parameters. In summary, this paper aims to provide professional and reliable quality data support for various business processes within the company, including material procurement, engineering construction, and equipment operation.

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“…The application of artificial intelligence to mathematical modeling has been further studied by researchers. Further prediction of the model through artificial intelligence has improved the accuracy and speed of prediction [45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Model for Steam Temperature Field of Downhole Multi-Thermal Fluid Generator

He,

Huang,

Dou

et al. 2024

Processes

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Aiming at the low efficiency of heavy-oil thermal recovery, a downhole multi-thermal fluid generator (DMTFG) can improve the viscosity reduction effect by reducing the heat loss of multi-thermal fluid in the process of wellbore transportation. The steam generated by the MDTFG causes damage to the packer and casing, owing to the return upwards along the annular space passage of the oil casing. To mitigate this damage, a heat transfer model for multi-channel coiled tubing wells and a prediction model for the upward return of the steam temperature field in the annulus were established with the basic laws of thermodynamics. Models were further verified by ANSYS. The results indicate the following four conclusions. First of all, when the surface pressure is constant, the deeper the located DMTFG, the shorter the distance for the steam to return would be. It is easier to liquefy the steam. Second, the higher the temperature of the steam produced by the downhole polythermal fluid generator, the larger the upward distance of the steam would be. Third, the higher the steam pressure at the outlet of the downhole polythermal fluid generator, the smaller the distance of steam upward return would be. Finally, the larger the diameter of the multi-channel conversion piping, the greater the distance of the steam return would be. It is meaningful to provide valuable theoretical guidance for packer position designing in the field. Meanwhile, the study also provides a modeling basis for the subsequent study of artificial intelligence in the downhole temperature field.

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Universal workflow of artificial intelligence for energy saving

Cited by 31 publications

References 110 publications

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Prediction of Road Network Speed around Sports Events Based on Random Forest Model

A Novel Prediction Model for Steam Temperature Field of Downhole Multi-Thermal Fluid Generator

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