COMMIT-NRG

Computational intelligence methods in time-series analysis and forecasting with application to energy management systems

Coordinatore	ALGARVE STP - PARQUE DE CIENCIA E TECNOLOGIA DO ALGARVE    more  Organization address address: "Universidade do Algarve, Campus de Gambelas Pav. A5" city: FARO postcode: 8005-139 contact info Titolo: Prof. Nome: António Cognome: Ruano Email: send email Telefono: 351290000000 Fax: 351290000000
Nazionalità Coordinatore	Portugal [PT]
Totale costo	45˙000 €
EC contributo	45˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-ERG-2008
Funding Scheme	MC-ERG
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-05-01   -   2012-04-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ALGARVE STP - PARQUE DE CIENCIA E TECNOLOGIA DO ALGARVE  Organization address address: "Universidade do Algarve, Campus de Gambelas Pav. A5" city: FARO postcode: 8005-139 contact info Titolo: Prof. Nome: António Cognome: Ruano Email: send email Telefono: 351290000000 Fax: 351290000000	PT (FARO)	coordinator	45˙000.00

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 Obiettivo del progetto (Objective)

'The work programme herein proposed is composed of work packages from two research projects where the host institution is starting participation by the end of 2008. The projects consist of: (i) in a partnership with the Portuguese Power Grid Company (PPGC), obtaining electricity demand predictive models at a national scale, through the efficient use of computational intelligence methods. The resulting models will be employed in the PPGC dispatch centre to schedule the operation of electricity production plants; (ii) in a collaboration with the University of Algarve, develop model-based predictive control systems able to maximise people's thermal comfort in buildings, while simultaneously minimise the energy spent in achieving it. The goal is contributing with methods able to decrease energy consumption by climate conditioning devices in buildings, a measure which is estimated to potentially save 8% of the total energy consumption in the European Union. Regarding the first project, the role of the applicant is to supervise and actively participate in the research activities along with the team from the PPGC. In the second project he will participate in the majority of tasks with emphasis on those related to data acquisition, development of predictive models, and the development of the predictive control algorithm.

A substantial part of the work in both projects will be devoted to the tasks of building long-term predictive models from acquired data (weather, indoors environment, and electricity demand data). As a consequence, this constitutes an excellent opportunity to contribute in advancing the state of the art of applying computational intelligence techniques in the determination of dynamical non-linear predictive models from acquired data in the form of time series, but also to improve the important energy management systems just described. The new skills and experience the applicant gained in his previous fellowship should contribute to achieve these goals.'