1. Savona, 10 th April 2014 Smart City ex-post and ex-ante Assessment Framework

2. Contents “Smart City” Concept Smart City Energy Assessment Framework - SCEAF Methodological Approach Political Field of Action Energy and Environmental Profile Related Infrastructures and ICT Pilot Appraisal Conclusions

3. “Smart City” Concept Holistic approach of a “Smart City” with emphasis on the need to promote a sustainable-energy - oriented “Smart City” concept.

4. Smart City Energy Assessment Framework - SCEAF (1/3) Development of a methodological tool to be used to conduct a thorough analysis and assessment, in a coherent, transparent and integrated way, through appropriately designed indicators, of the ex-ante and ex- post status of a Smart City, in relation to Energy Optimization issues.

5. Smart City Energy Assessment Framework - SCEAF (2/3) Systematic and independent evaluation means of the actions taken towards energy efficiency in parallel with the transition to become a “Smart City”. Assessment tool that clearly indicates underperforming sectors, providing to authorities a clear overview of the city performance per sector, in order to be able to lead targeted energy action plans. City Level SCEAF Municipal Building Level SCEAF 2 Versions of SCEAF

6. Smart City Energy Assessment Framework - SCEAF (3/3) SCEAF pillars and axes – Whole City Level and Municipal Building Level

7. Methodological Approach (1/8) Quantitative & qualitative assessment. Criteria: Numeric (N), linguistic (L) and interval (I), according to their measuring scale. Input Data Weather Conditions data Energy Profile data Social Media data Energy Prices data Renewable Energy Production data

8. Methodological Approach (2/8) Smart City Energy Performance (SCEP) +Political Field of Action (PFA), +Environmental & Energy Profiles (EEP) +Related Infrastructures & ICT (I&I)) The weights should add to one and are determined by the Decision Maker involved. P 1,1, … P 1,3, P 2,1 … P 2,5, P 3,1 … P 3,4 represent the performance of the city on each of the pillars, based on the indicators-criteria per pillar.

9. Methodological Approach (3/8) OMIMS transforms its heterogeneous information into linguistic information, expressed by means of 2-tuple: an expression model composed by a linguistic term a numeric value assessed in [-0.5, 0.5). F. Herrera, L. Martinez, An approach for combining linguistic and numerical information based on 2-tuple fuzzy representation model in decision-making, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 8 (5) (2000) 539-562. Phase I: Unification of the information Phase II: Aggregation of the preferred values Phase III: Transformation into 2-tuple http://omims.ipapastamatiou.gr/

10. Methodological Approach (4/8) Phase I: Unification of the information - Numerical Values [0,1] In the implementation of the specific transformation, we consider the graph of the chosen Basic Linguistic Term Set (BLTS) using the triangular fuzzy numbers. The value of each real number γ for every term of the fuzzy set, arises from the intersection points of the straight line x=θ and the edge of every triangle determined by the above triangular fuzzy numbers.

11. Let S={l 0,…,l p } and ST={s 0, …, s g }, two linguistic term sets such as g≥p Then, the linguistic transformation τ SSt is defined as: where μ li(.) and μ sk(.) are the membership functions associated with the term l i και s k respectively. p ) and the second input is the linguistic value of the specific set. In case the g-term BLTS is the same with the declared one, the values of the fuzzy set, will be si =1, si-1 =0,5 and si+1 =0,5 ( where i the linguistic value of the variable ). Whereas in case the two fuzzy sets are different, then the method used to make the above analysis is similar to that of the numerical values. More specifically instead of searching for the interception points of a straight line and the edges of each triangle, the software searches for the interception points of the chosen triangle,of the p-term fuzzy set and the edges of each triangle of the g-term BLTS. That is done by equating the increasing edge of the old triangle with both increasing and decreasing edges of each new triangle and the same thing for the decreasing edge. For each triangle the common points may vary from 0 to 2. In the last case of 2 common points, the software keeps in the fuzzy set the maximum of the computed values. Phase I: Unification of the information - Linguistic Terms Methodological Approach (5/8)

12. an interval valued in [0,1], in order to achieve this transformation we assume that the interval has a representation, derived by the membership function as seen below: The function τ ISt transforms an interval Ι into the fuzzy set ST is: where μ Ι (.) and μ sk (.) are membership functions associated with interval I and the s k terms, respectively. Phase I: Unification of the information - Interval Values Methodological Approach (6/8)

13. The aggregation is a simple procedure, which is used in order to acquire from the different fuzzy sets of every variable, a final fuzzy set. The final fuzzy set, which is unique for every solution, comes from the average value of the specific linguistic term of each variable. In that case the software follows the exact algorithmic procedure. Phase II: Aggregation Methodological Approach (7/8)

14. A function χ transforms a fuzzy set into a numerical value in the interval of granularity of S T, [0,g].: where the fuzzy set derives from Therefore, implementing definition 2, in other words the Δ function to β we shall obtain a collective preference relation whose values are expressed by 2-tuple: Phase III: Transformation into 2-tuple Methodological Approach (8/8)

15. Political Field of Action (1/3) 1.1 Degree of Ambition CO 2 reduction target of the city (or municipal buildings) till 2020 % of total emissions N Energy consumption reduction target of the city (or municipal buildings) till 2020 % of total consumption Peak electricity demand reduction target of the city till 2020 % of maximum load Renewable energy sources in the final use target of the city (or municipal buildings) till 2020 % of total energy consumption mix     1.1.2 1.1.31.1.4 NNN 1.1.1 only for City Level SCEAF

16. Political Field of Action (2/3) 1.2 Efficiency at Fulfilling Targets Medium term results for CO 2 reduction in the city (or municipal buildings) % of total goal 1.2.1 Medium term results for energy consumption reduction in the city (or municipal buildings) % of total goal Medium term results for peak electricity demand reduction in the city % of total goal Medium term results for renewable energy sources in the final use in the city (or municipal buildings) % of total goal     1.2.2 1.2.31.2.4 NNN N only for City Level SCEAF

17. Political Field of Action (3/3) 1.3 Asset Management Cost reduction for electrical energy needs in the city (or municipal buildings) € per m 2 (compared to the last energy bill records) 1.3.1 Cost reduction for fossil fuel energy needs in the city (or municipal buildings) € per m 2 (compared to the last energy bill records) Level of switching energy providers (electricity/gas) Flexibility of switching between energy providers based on price, consumption peaks, etc. Funds devoted for renewable energy sources & energy efficiency euros per capita, the funds to be given to energy efficiency and Renewables investments     1.3.2 1.3.31.3.4 N NLN

18. Energy and Environmental Profile (1/6) 2.1 Energy Consumption Intensity Energy consumption reduction in city (or municipal buildings) per capita kWh saved per number of inhabitants 2.1.1 Percentage reduction of fossil fuels in energy mix % reduction of previous fossil fuel energy consumption Percentage of electricity in energy mix % of total energy consumption    2.1.2 2.1.3 NN N

19. Energy and Environmental Profile (2/6) Energy consumption reduction in public transport per capita toe saved from buses and taxis per number of inhabitants    2.1.4 Energy consumption reduction in municipal fleet per capita toe saved from Road graders, Excavators, Buses, etc. per number of inhabitants Energy consumption reduction in Water treatment units, Recycling centers, Composting plants and Sewage treatment units per capita toe saved per number of inhabitants 2.1.52.1.6 I IN only for City Level SCEAF 2.1 Energy Consumption Intensity

20. Energy and Environmental Profile (3/6) 2.2 Energy Production via Renewable Technology Installed capacity (PV, Wind) kW per number of inhabitants 2.2.1 RES production intensity kWh per inhabitant (for the City Level SCEAF) kWh per m 2 (for the Municipal Buildings SCEAF) Percentage of RES in energy mix % of total energy consumption       2.2.2 2.2.3 N NN only for City Level SCEAF

21. Energy and Environmental Profile (4/6) 2.3 Energy Conservation Features Ability of storing electricity produced (electrical storage - batteries, hydrogen etc.) % of total electrical energy production 2.3.1 Ability of storing thermal power produced (thermal storage - boilers etc.) % of total thermal energy production Cogenerating Heat and Power % of total electricity generation Exploitation of weather conditions to optimize energy performance in municipal buildings Installed infrastructure exploiting weather conditions for energy conservation     2.3.2 2.3.32.3.4 I I N L

22. Energy and Environmental Profile (5/6) 2.4 Network efficiency Efficiency of transaction % efficiency 2.4.1 Efficiency of transmission % efficiency Reduced heat losses from district heating network % losses reduction compared to the last energy consumption records       2.4.2 2.4.3 NNN

23. Energy and Environmental Profile (6/6) 2.5 Ambient air pollution Emissions reduction per capita tn of CO 2 not emitted (compared to the last records) per inhabitant 2.5.1 Reduction of CO 2 Emission intensity % reduction of emissions (in tn) per kWh of energy consumed (electrical/fossil fuels) compared to the last records 2.5.2   N N

24. Related Infrastructures and ICT (1/4) 3.1 Monitoring Systems and BEMS Environmental Monitoring Systems Monitoring ambient temperature, solar radiation, wind conditions, humidity etc in the city 3.1.1 Air pollution Monitoring Systems Monitoring CO 2 emission levels in the city BEMS Use of BEMS or other systems monitoring energy consumption in the city    3.1.2 3.1.3 L LL Monitoring Systems and BEMS Monitoring ambient/indoor temperature, Monitoring solar radiation, Monitoring energy consumption in municipal buildings, BEMS, etc. 3.1.1 L  for the Municipal Buildings SCEAF for the City Level SCEAF only for City Level SCEAF

25. Related Infrastructures and ICT (2/4) 3.2 Level of Integration of Automations, Smart Meters and ICT solutions Controlled outdoor lighting on roads Using time switches or sensors to control road lighting in the city 3.2.1 Smart meters and automations Owning smart thermostats, motion-sensitive lighting, system controlling energy-intensive devices, etc. in municipal buildings System providing pricing information Owning system providing pricing information (gas/electricity) for the municipal buildings Multi-carrier energy network plants Using multi-carrier energy network plants to convert remaining energy into directly useable forms (gas/electricity) in city level      3.2.2 3.2.33.2.4 L L L L

26. Related Infrastructures and ICT (3/4) 3.3 Forecasting Systems Forecasting energy consumption Owning and using system providing energy consumption predictions (municipal buildings level) 3.3.1 Forecasting temperature Owning and using system providing weather predictions (city level) Forecasting CO2 levels Owning and using system providing energy CO2 level predictions (city level) Scheduling huge energy demanding operations Owning and using a system providing predictions related to energy consumption outliers (municipal buildings level)     3.3.2 3.3.33.3.4 LLLL for the City Level SCEAF

27. Related Infrastructures and ICT (4/4) Municipal Buddings' surveillance strategies Campaigns and information providing with the use of social media (Facebook, Twitter, YouTube) 3.4.1 3.4 Exploitation of Social Media L  3.3 Forecasting Systems Forecasting systems Energy consumption, energy production & temperature 3.3.1 L  for the Municipal Buildings SCEAF

28. Pilot Appraisal (1/4) Values of The SCEAF Indicators For Each City City 1City 2City 3 Strategy 1.1.10.22 0.15 1.1.20.150.160.12 1.1.30.32, 0.350.45, 0.490.29, 0.32 1.2.10.060.090.08 1.2.20.060.080.07 1.2.30.180.280.13 1.3.10.1, 0.120.13, 0.160.08, 0.11 1.3.2HMM 1.3.30.54, 0.550.65, 0.680.45, 0.47 Energy Profile 2.1.10.650.720.68 2.1.20.120.15 2.1.30.050.06 2.2.10.25 0.30 2.3.10.34, 0.380.26, 0.280.26, 0.29 2.3.20.15, 0.1800.17,0.19 2.3.3MMH Related Infrastructures-Energy and ICT 3.1.1MHM 3.2.1MHM 3.3.1LMM

29. Pilot Appraisal (2/4) Screenshot of the total solution, of the Strategy Sub problem

30. Pilot Appraisal (3/4) Multiple Screenshots of the software

31. Pilot Appraisal (4/4) City 1 is the most Optimus city of the above three. Concerning the general Strategy, it is on the same level with City 2. Concerning the Related Infrustructures- Energy and ICT, it is in the lowest level of all. That way the administrators of the city, are able to acknowledge its weaknesses and consider ways to amend them. Of course the above example, is just a demonstration of OMIMS potentials. Expert DSS analysts are able to design advanced problems, by using linguistic variables of different term sets and by using BLTS with more linguistic values.

32. Conclusions Effective framework for assessing the performance of a City in terms of energy optimization, CO 2 emissions reduction and energy cost minimization. Using appropriate indicators, the progress of a city in that direction can be revealed by analysing and evaluating its ex-ante and ex-post status across three axes: “Political Field of Action”, “Energy and Environmental Profile” and “Related Infrastructures and ICT”. Although the framework is designed for the evaluation of the city as a whole, it can be also customized per sector, such as municipal buildings, providing more focused information. it is suggested to the users to experiment and try to use the software in all of their non-homogeneous DSS problems.

33. Dr. Haris Doukas: h_doukas@epu.ntua.gr Mr. Vangelis Spiliotis: spiliotis@fsu.gr Mr. Vangelis Marinakis: vmarinakis@epu.ntua.gr Ms. Stella Androulaki: standrul@epu.ntua.gr Mr. Manos Ergazakis: mergaz@epu.ntua.grh_doukas@epu.ntua.grspiliotis@fsu.grvmarinakis@epu.ntua.grstandrul@epu.ntua.grmergaz@epu.ntua.gr Thank you for your Attention!