1. Savona, 10-04-2014 WP3 Optimus DSS

2. TECNALIA LA SALLE POLITO April 2014 MS4 August 2014

3. TECNALIA LA SALLE POLITO April 2014 MS4

4. TECNALIA LA SALLE POLITO April 2014 MS4

5. WP interrelationships

6. WP1WP2WP3WP4 Sant- Cugat Zaanstad Savona Feedback Survey SCEAF Mock-Up Weather forecasting data capturing (T2.2) Decentralized data capturing (T2.3) Social media/mining data capturing (T2.4.) Energy prices data capturing (T2.5) Energy production data capturing (T2.6) DSS Architecture Data (Modules) DSS DSS Functionalities Specifications

7. DSS ARCHITECTURE Weather Energy profiles Social media Energy prices RES production T3.1 Semantic Integration T2.1 Data Classification (Shared vocabulary) DSS Engine (T3.2, T3.3) Data portal Admin. environment DSS End-user interface End-users T3.4 T2.2-2.6

8. Weather Energy profiles Social media Energy prices RES production T3.1 Semantic Integration T2.1 Data Classification (Shared vocabulary) DSS Engine (T3.2, T3.3) Data portal Admin. environment DSS End-user interface End-users T3.4 T2.2-2.6 DSS ARCHITECTURE

9. T1.3 Mock-up T1.3 Survey identifying data WORK IN PROGRESS

10. Savona, 11-04-2014 T3.1 - Semantic Framework For Data Integration

11. Background Climate data Energy Prices Energy Consumption Social Media Renewable energy production DSS Analysis Forecast

12. Background Climate data Energy Prices Energy Consumption Social Media Renewable energy production DSS Analysis Forecast 1. Multiple names for the same thing 2. Relations between data sources

13. Background Climate data Energy Prices Energy Consumption Social Media Renewable energy production DSS Analysis Forecast Semantic technologies to solve heterogeneous data sources integration

14. Background Semantic technologies to solve heterogeneous data sources integration -Using standard vocabulary / ontology to define the data sources (e.g. Semanco ontology, Sensor Ontology, ISO/CEN standards…) provided by T2.1 -Generating links between input data sources. (e.g. that building has a relation with that social event and this energy production…) -Applying semantic integration processes -transforming data into RDF -using SPARQL to query data

15. Approach Weather forecast Energy prices Energy consumption Social media Available RES Semantic framework Data mining Metadata (patterns, clusters,…) Rules Inference Engine Front-end environment Admin interface End-user WP3 DSS T2.2T2.3T2.4T2.5T2.6 T3.1 T3.2 T3.4 T3.5 T3.3 T3.4 WP2 Data capture modules

16. Approach Data Capturing (WP2) Test cases (T3.1) Semantic integration process (T3.1) Data Cleaning Data Linking Data Enrichment Data Publishing HOW? Which channel, format, time frequency…? (T2.1) Data source (relational database, CSV, Sensor…) RDF Converter (D2R Server, Ontop, …) RDF dump Mappings between data sources and target ontologies (Issue 1) 1. Multiple names for the same thing

17. Approach Data source 1 RDF Data source 3 RDF Data source 4 RDF Data source 5 RDF Link discovery tool (e.g. Silk, Limes) Data source 2 Data sources connected (Issue 2) 2. Relations between data sources

18. Previous experience SEMANCO Project (www.semanco-project.eu)www.semanco-project.eu Integration of data source from multiple domains by means of semantic technologies. Developments of tools to support integration process

19. Savona, 10-04-2014 T 3.2 - DATA MINING ANALYSIS & T 3.3 - INFERENCE RULES

20. Introduction and open issues Tasks of the DSS, are: The topics of those tasks are: -T3.2 Data Mining Analysis and 1. Data mining for data analysis and forecasting. -T3.3 Inference Rules. 2. Intelligent rules for DSS operations. The work to be carried out in both tasks should be based on the WP1 findings: -D1.1 OPTIMUS Approach to Smart Cities and Energy Optimization. -D1.2 SCEAF. -D1.3 User requirements (Figure 1 in the next slide). Goals (work to be carried out by partners ): -Providing answers to open issues  What will be the OPTIMUS DSS -Agreeing on a common vocabulary  What do we mean by intelligent rule, … -Agreeing on an approach to be adopted in the DSS development  What to do in each task, relations between DSS modules…. -Identifying existing technologies we can apply  Data mining methods…

21. Open issues The vision of the DSS should be aligned with: 1. The work carried out in WP1: -Linking Smart Cities with Energy Optimization T1.1. -SCEAF T1.2. -User requirements T1.3. 2. The available technology which partners can master: data mining methods, inference engines…

22. Open issues Issues to discuss -Conceptual. -Technological (infrastructure). Conceptual: 1. Main goal the DSS will: -Store and visualize historical data (gathered with WP2 modules). -Forecast some indicators (energy demand, occupation, energy production, energy prizes…) based on the historical data  What-if scenarios/actions plans. -Review every week the effectiveness of the actions proposed by the DSS. On this basis the same actions will or will not be implemented the following period. 2. Architecture design of DSS considering Semantic Web technologies approach: -SW technologies  Model the data (sensors, weather, prizes, energy production, KPIs…) in RDF.  The data coming from the WP2 modules should be transformed into RDF.

23. Open issues Other conceptual issues, are: 3.What is exactly an intelligent rule? (to discuss) 4.What are the implications of applying intelligent rules in the data mining processes? (to discuss) Technological (infrastructure): 1.About DSS deployment, what are the implications for data mining processing? DSS will be a customizable Web site for each city:  Apache-like web server Data mining / Inference engine  require computation time  Web application server (Tomcat)  Data mining applications  web server as an independent Java applications.

24. Open issues 2.How we manage the time expended between start and completion of a data mining process? DSS  Need to execute data mining methods and run the inference engine when new data will come. May be it last 1h, 2h…. what would see/do the user while this happens?

25. Data mining techniques Tasks involved Task 3.2: Data Mining Analysis According to DoW: “…the main outcome is a metadata layer generated from data mining analysis to enhance the data source provided by the semantic framework (T3.1). This layer is based on patterns, clusters, trends, behaviour, and correlations which will be used by the inference engine in T3.3, and also displayed in the end-user environment to be developed in T3.4.” Task 3.3: Inference Rules According to DoW: “to develop and implement all the knowledge and intelligent rules for the energy optimization, based on the data that the DSS will receive as input, and to realize the so- called inference engine.”

26. Data mining techniques Relevant issues There are different methods for analysing building-related data for evaluating and improving building energy performance  Possible Classification: 1.Based on typical performance indicators To evaluating the energy performance between different buildings  KPIs normalize energy consumption (towards floor area, inhabitants, CDD) could be sufficient. 2.Statistical analysis Regression analysis: based on environmental data or building physical parameters, and to predict among other parameters (temperature, humidity, etc.) Correlation analysis: used to identify the relationship between building energy consumption and influencing factors such as climate, building physical parameters, occupancy patterns, and HVAC system design and operation.  Problem: when the data sample is getting big, it becomes more difficult to obtain statistical significance between the variables. Solution: Keep only the most recent data. 3.Building simulation Based on simulate building energy consumption under various conditions in order to identify the relationship between building energy consumption and different influencing factors (e.g., total building energy consumption and building relative compactness, or heating/cooling loads and building control strategies).

27. Data mining techniques Relevant issues The DSS of the OPTIMUS project will implement statistical analysis methods Issues for the election of the most appropriate forecasting model, are:  Efficiency in regard of the Forecasting Horizon (FH)  ARIMA and ARX models are usually accurate only for short-term forecasts Models used to predict energy demand in buildings can be divided into: 1.Regression models 2.Artificial Neural Network models (ANN) models 3.Models based on decision trees  These methods can help to understand the energy profile of the building and define some energy-saving solutions.  Other time-series forecasting models can be analyzed

28. Examples of DSS in other projects EnRiMa Aims to develop a decision-support system (DSS) for operators of energy-efficient buildings and spaces of public use This project provides a flexible solution based on energy-flow modelling and stochastic optimisation integrated with ICTs They have developed mathematical formulas for the energy-balance constraints based on previous works and different bibliography DSS is divided into two optimization models: Short-term (operational: used for planning the operation of the installed devices for the next period) Long-term (strategic: used for deciding investments into new equipment). They based on balance constraints and upper-level operational decision variables (DVs) as a valid approach also adopted by the most Optimisation-based treatments, for example, to adapt large-scale models to the building level.

29. Examples of DSS in other projects E N PROVE It consists of two modules: a.Energy Prediction Decision Support System (EPDSS) b.Building Performance & Usage Auditing In turn, the EPDSS (a) is divided into five modules: Scenario Creation, Prediction Engine, Decision Support, Solution Specification and Project Assistant. (Prediction Engine  implements all the energy demand and consumption prediction algorithms of the building divided into two groups: lighting and HVAC) These modules enable the calculation of the energy consumption of a building as well as the generation of: 1.Strategies for renovation. 2.Solutions on how energy consumption can be reduced implementing the strategies proposed.

30. Savona, 11-04-2014 T3.4 - Front-End Environments

31. Environments Three different environments will be implemented: 1.Data portal where the data captured in WP2 will be available to the city. Semantic search functions will be provided which take advantage of the data integration carried out in T3.1. Data will be published following linked data principles. 2.DSS management environment where an administrator person can customize and improve the generation of rules and the analysis processes. 3.End-user web environment where the final users will be able to understand the recommendations offered by the DSS and react to them. Implementation different forms of visualization and reporting which enable ex and post-ante comparisons as well as what-if scenarios. The visualization of the data will be based on simple representation such as histogram, spider, scatter, and timeline. Advanced representations will be implemented based on existing ones such as radial- graph, tree maps, networks and stacked flows.

32. Creation process This task will be carried out in a three-step approach: 1.Step 1 – Mock-up creation: The first step includes creation of a mock-up for each environment which includes the requirements collected in T1.3, the data collected in WP2, and the outputs of the analysis and rules of the T3.2 and T3.3. The final user will validate these mock-ups providing suggestions. THIS TASK IS ALREADY IN-PROGRESS 2.Step 2 – Environments development: Each environment will be developed as a web application based on standard languages such as HTML, CSS, JavaScript and open source libraries to deal with semantic data. The environments will be implemented following the responsive web design approach allowing the most frequently used devices such as computers, tablets and mobile phones. 3.Step 3 – Validation with DSS and Users: The development of the environments will be incremental. Once iteration is implemented, it will be validated with the final users using the available data. THIS TASK IS ALREADY IN-PROGRESS

33. Data portal Data source 1 RDF Data source 3 RDF Data source 4 RDF Data source 5 RDF Data source 2 DSS ENGINE Data Portal FINAL USERS T3.1

34. Management environment Semantic framework Data mining Metadata (patterns, clusters,…) Rules Inference Engine Front-end environment Admin interface T3.1 T3.2 T3.4 T3.5 T3.3 T3.4 FINAL USERS City Administration customizing and improving the generation of rules and the analysis processes.

35. End-user web environment Semantic framework Data mining Metadata (patterns, clusters,…) Rules Inference Engine Front-end environment Admin interface T3.1 T3.2 T3.4 T3.5 T3.3 T3.4 FINAL USERS T1.3 Mock-up

36. Previous experience SEMANCO Project (www.semanco-project.eu)www.semanco-project.eu Mockup version 1 Mockup version 2 Mockup version 3 Current platform

37. Conclusions We need to successfully complete the first stage of the DSS mock-up development in each city. We need the inputs from POLITO, NTUA, TECNALIA about the technical specifications of the DSS FUNITEC can collaborate in T2.1 providing top-level specifications of the DSS architecture (format of the outputs, installation of software components (Virtuoso, Rapidminer….). We need to agree what is the DSS architecture: is it a diagram? Are the specifications to connect components? Are top-level module specifications? We need to appoint the persons in charge of the development of the modules, to work in a coordinated manner. We need from T2.1 the naming conventions for the DSS data

38. Questions? FUNITEC / La Salle BCN Campus ARC Research Group Leandro Madrazo / madrazo@salleurl.edu Álvaro Sicilia / asicilia@salleurl.edu Gonçal Costa / gcosta@salleurl.edu