Energy analysis, forecasting and optimization methods play a fundamental role in managing Combine Heat and Power (CHP) systems for energy production, in order to find the most suitable operational point. Indeed, several industries owning such cogeneration systems can significantly reduce overall costs by applying diverse techniques to predict, in real-time, the optimal load of the system. However, this is a complex task that requires processing a large amount of information from multiple data sources (IoT sensors, smart meters and much more), and, in most of the cases, is manually carried out by the energy manager of the company owning the CHP. For this reason, resorting to machine learning methods and new advanced technologies such as fog computing can significantly ease and automate real-time analyses and predictions for energy management systems that deal with huge amounts of data. In this paper we present GEM-Analytics, a new platform that exploits fog computing to enable AI-based methods for energy analysis at the edge of the network. In particular, we present two use cases involving CHP plants that need for optimal strategies to reduce the overall energy supply costs. In all the case studies we show that our platform can improve the energy load predictions compared to baselines thus reducing the costs incurred by industrial customers.

GEM-Analytics: Cloud-to-Edge AI-Powered Energy Management / Tovazzi, D.; Faticanti, F.; Siracusa, D.; Peroni, C.; Cretti, S.; Gazzini, T.. - 12441:(2020), pp. 57-66. ((Intervento presentato al convegno 17th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-63058-4_5].

GEM-Analytics: Cloud-to-Edge AI-Powered Energy Management

Faticanti F.;Siracusa D.;
2020

Abstract

Energy analysis, forecasting and optimization methods play a fundamental role in managing Combine Heat and Power (CHP) systems for energy production, in order to find the most suitable operational point. Indeed, several industries owning such cogeneration systems can significantly reduce overall costs by applying diverse techniques to predict, in real-time, the optimal load of the system. However, this is a complex task that requires processing a large amount of information from multiple data sources (IoT sensors, smart meters and much more), and, in most of the cases, is manually carried out by the energy manager of the company owning the CHP. For this reason, resorting to machine learning methods and new advanced technologies such as fog computing can significantly ease and automate real-time analyses and predictions for energy management systems that deal with huge amounts of data. In this paper we present GEM-Analytics, a new platform that exploits fog computing to enable AI-based methods for energy analysis at the edge of the network. In particular, we present two use cases involving CHP plants that need for optimal strategies to reduce the overall energy supply costs. In all the case studies we show that our platform can improve the energy load predictions compared to baselines thus reducing the costs incurred by industrial customers.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
svn
Springer Science and Business Media Deutschland GmbH
978-3-030-63057-7
978-3-030-63058-4
Tovazzi, D.; Faticanti, F.; Siracusa, D.; Peroni, C.; Cretti, S.; Gazzini, T.
GEM-Analytics: Cloud-to-Edge AI-Powered Energy Management / Tovazzi, D.; Faticanti, F.; Siracusa, D.; Peroni, C.; Cretti, S.; Gazzini, T.. - 12441:(2020), pp. 57-66. ((Intervento presentato al convegno 17th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-63058-4_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/297096
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