Smart energy monitoring and management system for industrial applications

Collins, Keith; Mallick, Madhumita; Volpe, G.; Morsi, Walid G.

doi:10.1109/epec.2012.6474987

Cited by 15 publications

(2 citation statements)

References 7 publications

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“…From a different aspect, Dobrin et al [16] combined offline scheduling and fixed-priority scheduling to achieve flexibility while coping with complex timings of different tasks. Others have viewed the problem of load scheduling from the angle of variable electricity prices such as Time-of-Use and Real-Time Pricing [17], [18].…”

Section: Demand Response For Industrial Plantsmentioning

confidence: 99%

Maintenance-centric energy management of industrial plants assisted by demand response

Mohagheghi

Raji

2014

2014 IEEE Industry Application Society Annual Meeting

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Asset management is an integral part of every industrial plant. Here, the goal is to improve the efficiency of the system while reducing the cost of maintenance and risk of failure. Traditionally, asset management has been performed as time-based maintenance or corrective maintenance; however, in order to reduce the consequences of component failures on the operation of the overall system many plant managers have moved towards more advanced condition-based or reliabilitycentric maintenance techniques. Here, the asset manager would adopt a more proactive maintenance approach based on the condition of the component and its importance for the overall production plan. Although shutting down a process for maintenance purposes has long-term benefits, it may accrue costs associated with the lost revenue, inventory buildup or wasted labor. A solution is put forth in this paper that counteracts financial losses due to preventive maintenance with direct benefits gained from demand response (DR). Within this context, maintenance scheduling is coordinated with DR, which is used as a tool at the asset manger's disposal in order to offset these financial losses. A case study is presented for a sample industrial plant in order to further explain the design concepts.Index Terms-Asset management, demand response, energy management, industrial plant demand scheduling, maintenance scheduling, preventive maintenance.

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Section: Demand Response For Industrial Plantsmentioning

confidence: 99%

Maintenance-centric energy management of industrial plants assisted by demand response

Mohagheghi

Raji

2014

2014 IEEE Industry Application Society Annual Meeting

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“…Sistem ini juga masih bersifat pasif karena belum bisa mengirimkan notifikasi ke pengguna. Mengembangkan sistem pemantauan sumber daya listrik pada industri [11]. Kelebihan daripada sistem Pemantauan programmable logic controller berbasis internet of things dengan menggunakan sistem notifikasi ini adalah mampu menampilkan daya listrik yang dikonsumsi mesin dalam bentuk dollar.…”

Section: Pendahuluanunclassified

Pemantauan programmable logic controller berbasis internet of things dengan menggunakan sistem notifikasi

Tanra¹,

Pahmi²,

Arsad

2023

tekno

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Di era Industri 4.0, sistem pemantauan mesin berbasis IoT mutlak diperlukan sehingga mesin bisa bekerja dengan cerdas dan kegagalan mesin bisa dideteksi dari awal. Pemantauan industri sangat diperlukan untuk mengetahui kondisi mesin yang selalu berubah secara drastis dan dapat mempengaruhi kinerja produksi. Pemantauan mesin di Industri bermanfaat untuk memenuhi target produksi yang tinggi, mengurangi konsumsi daya listrik, meningkatkan kualitas dan manajemen industri secara global. Tujuan pada penelitian ini adalah merancang sebuah sistem pemantauan mesin berbasis PLC dan sistem pencatatan berbasis raspberry pi yang terhubung menggunakan aplikasi Whatsapp. Sebuah grup whatsapp yang berisi beberapa orang pengguna akan menerima notifikasi kondisi mesin melalui grup. Hal ini sangat bermanfaat apabila terjadi keadaan darurat di mesin, maka semua pengguna di grup tersebut bisa langsung mengetahuinya. Berdasarkan hasil penelitian, sistem ini mampu merespon pembacaan PLC dengan rata-rata 1.84 detik dan penerimaan pesan di whatsapp dengan rata-rata 4.43 detik. Hal ini memungkinkan untuk memantau kondisi mesin dengan cepat dan tepat

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Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management

Rajaoarisoa

2020

Artificial Intelligence Techniques for a Scalable Energy Transition

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Smart energy monitoring and management system for industrial applications

Cited by 15 publications

References 7 publications

Maintenance-centric energy management of industrial plants assisted by demand response

Maintenance-centric energy management of industrial plants assisted by demand response

Pemantauan programmable logic controller berbasis internet of things dengan menggunakan sistem notifikasi

Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management

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