Prediction of Energy Consumption in Digital Twins of Intelligent Factory by Artificial Intelligence

2021 ◽  
Author(s):  
Jingyi Wu ◽  
Yukun Dang ◽  
Hongxiang Jia ◽  
Xinyue Liu ◽  
Zhihan Lv
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Digital Twins

Artificial intelligence in the digital twins: State of the art, challenges, and future research topics

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 12
Author(s):  
Zhihan Lv ◽  
Shuxuan Xie
Keyword(s):  
Artificial Intelligence ◽  
Smart City ◽  
Traffic Accidents ◽  
Intelligent Manufacturing ◽  
Unmanned Vehicles ◽  
Urban Traffic ◽  
Future Research ◽  
Test Accuracy ◽  
Digital Twins ◽  
The Future

Advanced computer technologies such as big data, Artificial Intelligence (AI), cloud computing, digital twins, and edge computing have been applied in various fields as digitalization has progressed. To study the status of the application of digital twins in the combination with AI, this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature. We discuss the application status of digital twins in the four areas of aerospace, intelligent manufacturing in production workshops, unmanned vehicles, and smart city transportation, and we review the current challenges and  topics that need to be looked forward to in the future. It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation, failure warning, aircraft assembly, and even unmanned flight. In the virtual simulation test of automobile autonomous driving, it can save 80% of the time and cost, and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy. In the intelligent manufacturing of production workshops, the establishment of a virtual workplace environment can provide timely fault warning, extend the service life of the equipment, and ensure the overall workshop operational safety. In smart city traffic, the real road environment is simulated, and traffic accidents are restored, so that the traffic situation is clear and efficient, and urban traffic management can be carried out quickly and accurately. Finally, we looked forward to the future of digital twins and AI, hoping to provide a reference for future research in related fields.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

Digital Technology Advancements in Knowledge Management in Domestic Tour Products in the Russian Federation

2021 ◽  
pp. 135-148
Author(s):  
Vardan Mkrttchian ◽  
Viacheslav Voronin
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Supply Chain ◽  
Life Cycle ◽  
Russian Federation ◽  
Practical Implementation ◽  
Digital Twins ◽  
Software Modules ◽  
Multi Agent ◽  
The Russian Federation

This chapter discusses the capabilities with problem-oriented digital twin avatars, supply chain, volumetric hybrid, and federated-consistent blockchain use to the nature of knowledge. The goal of this chapter is a theoretical study and practical implementation in the form of basic models and software modules and artificial intelligence algorithms in managing the life cycle of an internal Russian tour product. A laboratory for digitization and management, using multi-agent models of intelligent digital twins-avatars, is created. The purpose of these studies is to solve a scientific problem.

scholarly journals Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3440 ◽  
Author(s):  
Chin-Chi Cheng ◽  
Dasheng Lee
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Air Conditioning ◽  
Prior Information ◽  
Energy Savings ◽  
Theoretical Derivation ◽  
Consumption Data ◽  
Temperature And Humidity ◽  
Hvac Control ◽  
Comparative Results

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

scholarly journals Development strategy formation of the Committee on Artificial Intelligence in the Scientific and Educational Center "Engineering of the Future"

2021 ◽  
Vol 11 (3) ◽  
pp. 260-293
Author(s):  
I.I. Barinov ◽  
◽  
N.M. Borgest ◽  
S.Y. Borovik ◽  
O.N. Granichin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Development Strategy ◽  
Knowledge Bases ◽  
Enterprise Management ◽  
High Tech ◽  
Digital Twins ◽  
Management Processes ◽  
The Future ◽  
Multi Agent ◽  
A Chain

The Scientific and Educational Center "Engineering of the Future", created on the basis of the Institute of Regional Development of the Samara Region, has formed a number of important sectoral and subject committees, in which it is planned to develop breakthrough technologies in high-tech industries. The Committee on Artificial Intelligence, organized within the framework of the SEC "Engineering of the Future", forms its development strategy. The article outlines the vision for the prospects of such a strategy of the project team, consisting of specialists from universities, academia, design organizations, commercial companies and startups. The key in the proposed strategy is emergent artificial intelligence - it is a spontaneously arising, under the influence of external events or from internal causes or motives, a chain of coordinated state changes by agents who find a solution to a new problem or increase the value of an existing solution. The authors believe that the construction of emergent artificial intelligence is based on multi-agent technologies and ontologies of subject areas. The article formulates the main tasks of the Committee for the coming years and presents a technological project. The project includes the creation of mass production of intelligent resource management systems, personalized by creating digital twins of enterprise management processes, knowledge bases and multi-agent technologies. The essence of the proposed project, reflecting the important priorities of industrial partners, is to create a line of intelligent products and services for all stages of the life cycle of complex high-tech products and build a "factory" of such systems in the form of an open instrumental platform that will allow these enterprises to reduce dependence on the solution provider and on their own develop and modernize such systems. The principles of the Committee's work were proposed, its first potential projects and planned cooperation on these projects to achieve the first practical results were considered.

scholarly journals Artificial intelligence technologies as a factor in increasing the economic efficiency of generating companies

2021 ◽  
Vol 288 ◽  
pp. 01067
Author(s):  
Elena Troianova ◽  
Evgenia Lerman ◽  
Elena Baliasnikova ◽  
Ina Fiutik ◽  
Ekaterina Savelieva
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Economic Efficiency ◽  
Energy Sector ◽  
Internal Factors ◽  
Production And Consumption ◽  
Factors Of Influence ◽  
Key Aspects

The article deals with the application of modern artificial intelligence technologies that affect the economic efficiency of generating companies. Scientific novelty lies in the approach to the consideration of artificial intelligence as both external and internal factors of influence on the dynamics of production and consumption of electricity. As a result of the study, the key aspects of the growth of the economic efficiency of the activities of energy generating companies are highlighted and characterized. The forecast of the prospective sustainable development of certain areas of the energy sector and the increase in energy consumption is presented.

Knowledge-based decision intelligence in street lighting management

2021 ◽  
pp. 1-19
Author(s):  
Cristóvão Sousa ◽  
Daniel Teixeira ◽  
Davide Carneiro ◽  
Diogo Nunes ◽  
Paulo Novais
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Light Quality ◽  
Communication Technologies ◽  
Decision Problems ◽  
Computational Power ◽  
Street Lighting ◽  
Knowledge Based

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

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