There is an increasing demand for industry specific solutions for optimizing production processes with the transitions towards Industry 4.0. The commercial greenhouse sector relies heavily on optimal use of energy with multiple new concepts introduced in recent years e.g. vertical farming and urban agriculture. Digital twins allow utilizing Internet of Things and big data to simulate the alternative operation strategies without compromising current operation. This paper aims to present the development of a digital twin of the commercial greenhouse production process as a part of the recently launched EUDP funded project Greenhouse Industry 4.0 in Denmark. This digital twin allows using big data and the Internet of Things to optimise the greenhouse production process and communicate with other digital twins representing essential areas in the greenhouse (climate and energy). This digital twin can estimate future states of the greenhouse by using past and real-time data inputs from databases, sensors, and spot markets. This paper also introduces a Smart Industry Architecture Model Framework for the discussion of the required data architecture of the digital twin for the greenhouse production flow which ensures a correct data architecture for the data exchange across all entities in the system.
|Titel||Proceeding of 2020 RIVF International Conference on computing and communic action Tecnologies|
|Status||Accepteret/In press - 2020|
|Begivenhed||THE 2020 RIVF INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION TECHNOLOGIES - Ho Chi Minh City, Vietnam|
Varighed: 6. apr. 2020 → 7. apr. 2020
|Konference||THE 2020 RIVF INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION TECHNOLOGIES|
|By||Ho Chi Minh City|
|Periode||06/04/2020 → 07/04/2020|
Bibliografisk noteCo-authors: Zheng Ma, Jesper Mazanti Aaslyng, Bo Nørregaard Jørgensen<br/>Same information as in: https://portal.findresearcher.sdu.dk/da/publications/agent-based-modeling-of-climate-and-electricity-market-impact-on- <br/>Conference Website: https://rivf2020.rmit.edu.vn/
Howard, D. A., Ma, Z., Aaslyng, J. P. M., & Jørgensen, B. N. (Accepteret/In press). Data Architecture for Digital Twin of Commercial Greenhouse Production. I Proceeding of 2020 RIVF International Conference on computing and communic action Tecnologies IEEE.