Information Fusion 29 (2016) 149–161 Contents lists available at ScienceDirect Information Fusion journal homepage: www.elsevier.com/locate/inffus How “OPTIMUS” is a city in terms of energy optimization? e-SCEAF: A web based decision support tool for local authorities I. Papastamatiou a , H. Doukas a,∗ , E. Spiliotis b , J. Psarras a a Decision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece b Forecasting and Strategy Unit, School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece article info Article history: Available online 22 October 2015 Keywords: Smart cities Energy assessment framework Decision support system Information fusion 2-Tuple linguistic model abstract Nowadays cities tend to become “Smarter”, usually disregarding the issues of energy efficiency and sustain- ability. Therefore, optimizing energy use in a city remains a challenge and respective decision support systems are important to guide local authorities toward that direction. This paper provides a holistic approach pre- senting a Smart City Energy Assessment Framework (SCEAF) along with a specific web based decision support tool, the so-called e-SCEAF, which can provide local authorities with fruitful results for assessing the energy behavior and performance of their city. The tool merges heterogeneous information, such as clearly quan- tifiable energy related indicators, the related city policy context performance and the integration of smart infrastructure. This multi-source information fusion is based on the 2-tuple linguistic representation model of Herrera and Martínez. This particular model has been widely used in decision problems and was mainly se- lected due to the fact that it provides linguistic results that are accurate and easy to understand by the cities’ local authorities. The performance, usefulness and effectiveness of the SCEAF framework and the e-SCEAF tool are tested on a real life application in three different cities, Savona (Italy), Sant Cugat del Vallès (Spain) and Zaanstad (The Netherlands). In this respect, the role of fusion methods and algorithms for merging multiple information will be evaluated in a “real life environment”. © 2015 Elsevier B.V. All rights reserved. 1. Introduction As indicated by the European Commission [1], in the EU en- ergy consumption in industry, transport and households accounts for nearly 80% of global atmospheric concentrations of greenhouse gases. The current status quo will deteriorate over the coming years as more than 80% of the European population is expected to live in urban areas by 2020 [2]. As a result, transforming urban areas into energy-efficient cities is the key for an ecological future and this has become a crucial part of Europe’s 2020 strategy [3]. In order for Eu- rope to achieve its goals, cities need to become “Smarter”. This refers to the optimization of energy use in the building and transport sec- tor and the exploitation of sustainable solutions through Information and Communication Technology (ICT). For instance, even though there are multi-source data (energy and other related) available to local authorities, there are currently no related systems able to multi-process all this information in a smart way. Cities need a system to collect, analyze and integrate multidis- ciplinary data in order to assess their functionalities and help them ∗ Corresponding author. Tel.:+30 6932441326, +30 210 7722083. E-mail addresses: ipapastamatiou@epu.ntua.gr (I. Papastamatiou), h_doukas@epu. ntua.gr (H. Doukas), spiliotis@fsu.gr (E. Spiliotis), john@epu.ntua.gr (J. Psarras). optimize their efficiency. Within this context, the “OPTIMUS” project [4] aims to develop a respective Decision Support System (DSS) which will assist the cities to decrease their energy consumption and CO 2 emissions, limit their energy costs and utilize ICT. However, in order to accurately assess the benefits achieved, a comparative anal- ysis is required between the ex-ante and the ex-post profiles of the cities. The first step toward the implementation of such a system and the optimization of energy use is to evaluate the present state of the city and indicate its strengths, its weaknesses and the opportunities arising. In that direction, the Smart City Energy Assessment Frame- work (SCEAF) [5] is applied to evaluate the cities in relation to three main axes/ disciplines: the “Political Field of Action”, the “Energy and Environmental Profile” and the “Related Infrastructures and ICT”. More specifically: • The first axis is used to evaluate the city regarding its energy sav- ings and environmental commitments. The SCEAF takes into ac- count both the targets set by the city and the efforts made so far in the direction of achieving them. Finally, the funds devoted for renewable energy sources and energy efficiency are considered. • The second axis assesses the city according to the energy con- sumption and CO 2 emissions levels. The energy produced via Renewable Energy Sources (RES) and Cogeneration Heat and Power (CHP) plants plays a significant role in the evaluation http://dx.doi.org/10.1016/j.inffus.2015.10.002 1566-2535/© 2015 Elsevier B.V. All rights reserved.