TY - JOUR
T1 - From big data to smart energy services
T2 - An application for intelligent energy management
AU - Marinakis, Vangelis
AU - Doukas, Haris
AU - Tsapelas, John
AU - Mouzakitis, Spyros
AU - Sicilia, Álvaro
AU - Madrazo, Leandro
AU - Sgouridis, Sgouris
N1 - Funding Information:
The work presented is based on research conducted within the framework of the projects “OPTIMising the energy USe in cities with smart decision support system (OPTIMUS)”, European Union Seventh Framework Programme , Greece, grant agreement № 608703 ( http://optimus-smartcity.eu/ ) and “EU-GCC Clean Energy Technology Network”, European Commission , Greece - FPI service contract number PI/2015/370817 ( http://www.eugcc-cleanergy.net ). The authors wish to thank Alfonso Capozzoli, Alice Gorrino and Vincenzo Corrado (Politecnico di Torino — Italy), Victor Martinez (Sant Cugat — Spain), Mansueto Rossi (UNIGE, Italy), Pietro Pera (Savona/IPS — Italy) and Alex Beijers (Zaanstad — the Netherlands), whose contribution, helpful remarks and fruitful observations were invaluable for the development of this work. The content of the paper is the sole responsibility of its authors and does not necessary reflect the views of the EC.
Funding Information:
The work presented is based on research conducted within the framework of the projects “OPTIMising the energy USe in cities with smart decision support system (OPTIMUS)” European Union Seventh Framework Programme, Greece, grant agreement №608703 (http://optimus-smartcity.eu/) and “EU-GCC Clean Energy Technology Network” European Commission, Greece - FPI service contract number PI/2015/370817 (http://www.eugcc-cleanergy.net). The authors wish to thank Alfonso Capozzoli, Alice Gorrino and Vincenzo Corrado (Politecnico di Torino — Italy), Victor Martinez (Sant Cugat — Spain), Mansueto Rossi (UNIGE, Italy), Pietro Pera (Savona/IPS — Italy) and Alex Beijers (Zaanstad — the Netherlands), whose contribution, helpful remarks and fruitful observations were invaluable for the development of this work. The content of the paper is the sole responsibility of its authors and does not necessary reflect the views of the EC.
Funding Information:
The work presented is based on research conducted within the framework of the projects ?OPTIMising the energy USe in cities with smart decision support system (OPTIMUS)?, European Union Seventh Framework Programme, Greece, grant agreement ?608703 (http://optimus-smartcity.eu/) and ?EU-GCC Clean Energy Technology Network?, European Commission, Greece - FPI service contract number PI/2015/370817 (http://www.eugcc-cleanergy.net). The authors wish to thank Alfonso Capozzoli, Alice Gorrino and Vincenzo Corrado (Politecnico di Torino ? Italy), Victor Martinez (Sant Cugat ? Spain), Mansueto Rossi (UNIGE, Italy), Pietro Pera (Savona/IPS ? Italy) and Alex Beijers (Zaanstad ? the Netherlands), whose contribution, helpful remarks and fruitful observations were invaluable for the development of this work. The content of the paper is the sole responsibility of its authors and does not necessary reflect the views of the EC.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2020/9
Y1 - 2020/9
N2 - Big data is an ascendant technological concepts and includes smart energy services, such as intelligent energy management, energy consumption prediction and exploitation of Internet of Things (IoT) solutions. As a result, big data technologies will have a significant impact in the energy sector. This paper proposes a high level architecture of a big data platform that can support the creation, development, maintenance and exploitation of smart energy services through the utilisation of cross-domain data. The proposed platform enables the simplification of the procedure followed for the information gathering by multiple sources, turning into actionable recommendations and meaningful operational insights for city authorities and local administrations, energy managers and consultants, energy service companies, utilities and energy providers. A web-based Decision Support System (DSS) has been developed according to the proposed architecture, exploiting multi-sourced data within a smart city context towards the creation of energy management action plans. The pilot application of the developed DSS in three European cities is presented and discussed. This “data-driven” DSS can support energy managers and city authorities for managing their building facilities’ energy performance.
AB - Big data is an ascendant technological concepts and includes smart energy services, such as intelligent energy management, energy consumption prediction and exploitation of Internet of Things (IoT) solutions. As a result, big data technologies will have a significant impact in the energy sector. This paper proposes a high level architecture of a big data platform that can support the creation, development, maintenance and exploitation of smart energy services through the utilisation of cross-domain data. The proposed platform enables the simplification of the procedure followed for the information gathering by multiple sources, turning into actionable recommendations and meaningful operational insights for city authorities and local administrations, energy managers and consultants, energy service companies, utilities and energy providers. A web-based Decision Support System (DSS) has been developed according to the proposed architecture, exploiting multi-sourced data within a smart city context towards the creation of energy management action plans. The pilot application of the developed DSS in three European cities is presented and discussed. This “data-driven” DSS can support energy managers and city authorities for managing their building facilities’ energy performance.
KW - Big data
KW - Decision support system
KW - Energy services
KW - Intelligent management
KW - Smart Cities
UR - http://www.scopus.com/inward/record.url?scp=85046718852&partnerID=8YFLogxK
U2 - 10.1016/j.future.2018.04.062
DO - 10.1016/j.future.2018.04.062
M3 - Article
AN - SCOPUS:85046718852
SN - 0167-739X
VL - 110
SP - 572
EP - 586
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -
