The OPTIMUS Decision Support System (DSS) developed within the FP7- Smatcities project OPTIMUS is a web-based tool addressed to city authorities to assist them in the decision making process, aimed at minimizing both energy use and CO2 emissions in public buildings. The OPTIMUS DSS receives input data belonging to different fields (weather, in-building sensors, social media, energy costs and energy production). The elaboration of these heterogeneous data through data mining techniques and inference rule processing provides to the stakeholders a set of actions regard the management of the HVAC systems, to be implemented within a week. The actions include the optimal start/stop of the heating system, the schedule of the indoor set point temperature according to the adaptive comfort concept and to real time feedback of the building occupants, the management of the air-side economizer, the load shifting related to the self consumption/selling of the energy produced through a PV system. The paper describes the architecture of the OPTIMUS DSS with particular regard to the modelling process of the actions. An example of action plan process is also presented and the first results are discussed.

MANAGEMENT STRATEGIES FOR THE ENERGY SAVING OF PUBLIC BUILDINGS THROUGH A DECISION SUPPORT SYSTEM / Capozzoli, Alfonso; Corrado, Vincenzo; Gorrino, Alice; Madrazo, Leandro; Piscitelli, MARCO SAVINO; Sicilia, Álvaro. - In: NEWDIST. - ISSN 2283-8791. - STAMPA. - Special Issue:(2016), pp. 406-415.

MANAGEMENT STRATEGIES FOR THE ENERGY SAVING OF PUBLIC BUILDINGS THROUGH A DECISION SUPPORT SYSTEM

CAPOZZOLI, ALFONSO;CORRADO, Vincenzo;GORRINO, ALICE;PISCITELLI, MARCO SAVINO;
2016

Abstract

The OPTIMUS Decision Support System (DSS) developed within the FP7- Smatcities project OPTIMUS is a web-based tool addressed to city authorities to assist them in the decision making process, aimed at minimizing both energy use and CO2 emissions in public buildings. The OPTIMUS DSS receives input data belonging to different fields (weather, in-building sensors, social media, energy costs and energy production). The elaboration of these heterogeneous data through data mining techniques and inference rule processing provides to the stakeholders a set of actions regard the management of the HVAC systems, to be implemented within a week. The actions include the optimal start/stop of the heating system, the schedule of the indoor set point temperature according to the adaptive comfort concept and to real time feedback of the building occupants, the management of the air-side economizer, the load shifting related to the self consumption/selling of the energy produced through a PV system. The paper describes the architecture of the OPTIMUS DSS with particular regard to the modelling process of the actions. An example of action plan process is also presented and the first results are discussed.
2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2653555
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