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Optimal Energy Management System Design for Smart Cattle Shed

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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2018)

Abstract

As the importance of energy consumption has recently increased, even Cattle shed facilities are using a lot of energy to grow livestock. However, as the energy consumption increases, wasted energy is generated, which is a big problem. To solve these problems, we designed the optimal energy management system using ICBM technology. Data from IoT sensors and controllers are collected within smart cattle shed and classified through analysis of Big Data in the database. The classification of data is from cloud systems to derive optimal energy management methods through SVR algorithms. And displays the results to the user on the mobile, allowing them to monitor the power value and control the device via the mobile. Through the designed system, the energy consumption of existing smart cattle shed compared to a barn applied with an optimal energy management system, there is not much difference when the usage is high. When the usage is low, it is set to use only the optimum energy and there are many differences. By analyzing the information of the surrounding environment through the design, it is possible to manage the optimum energy anytime and anywhere in real time, by providing power usage guidelines for smart cattle shed, energy usage and energy costs (power charges) can be saved.

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Acknowledgements

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2013-1-00877) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Correspondence to Hyun Yoe .

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Choe, H., Yoe, H. (2019). Optimal Energy Management System Design for Smart Cattle Shed. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2018. Studies in Computational Intelligence, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-319-98367-7_6

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