In this paper, a new open-source SW framework for energy management is presented. Its name is rEMpy, which stands for residential Energy Management in python. The framework has a modular structure and it is composed by an optimal scheduler, a user interface, a prediction module and the building thermal model. Unlike most of the EMs in literature, rEMpy is open-source, can be fully customized (in terms of tasks, modules and algorithms) and integrates in real-time a thermal modelling software. In this contribution, an overview of the rEMpy and its constitutive parts is given first, followed by a detailed description of the rEMpy modules and the communication system. The Computational Intelligence algorithms which perform forecasting, thermal modelling and optimal scheduling are also presented. The performance of rEMpy is finally evaluated in two case studies with different heating technologies and the results are reported and discussed.

A new open-source Energy Management framework: Functional description and preliminary results / Fagiani, Marco; Severini, Marco; Squartini, Stefano; Ciabattoni, Lucio; Ferracuti, Francesco; Fonti, Alessandro; Comodi, Gabriele. - ELETTRONICO. - (2017), pp. 1207-1214. (Intervento presentato al convegno 2017 IEEE Congress on Evolutionary Computation, CEC 2017 tenutosi a esp nel 2017) [10.1109/CEC.2017.7969443].

A new open-source Energy Management framework: Functional description and preliminary results

Fagiani, Marco;Severini, Marco;Squartini, Stefano;Ciabattoni, Lucio;Ferracuti, Francesco;Fonti, Alessandro;Comodi, Gabriele
2017-01-01

Abstract

In this paper, a new open-source SW framework for energy management is presented. Its name is rEMpy, which stands for residential Energy Management in python. The framework has a modular structure and it is composed by an optimal scheduler, a user interface, a prediction module and the building thermal model. Unlike most of the EMs in literature, rEMpy is open-source, can be fully customized (in terms of tasks, modules and algorithms) and integrates in real-time a thermal modelling software. In this contribution, an overview of the rEMpy and its constitutive parts is given first, followed by a detailed description of the rEMpy modules and the communication system. The Computational Intelligence algorithms which perform forecasting, thermal modelling and optimal scheduling are also presented. The performance of rEMpy is finally evaluated in two case studies with different heating technologies and the results are reported and discussed.
2017
9781509046010
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/252453
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