Front Cover -- Half Title -- RIVER PUBLISHERS SERIES IN RENEWABLE ENERGY -- Title -- iURBAN: Intelligent Urban Energy Tool -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1 -- Introduction -- Chapter 2 -- Logic Architecture, Components, and Functions -- 2.1 Logic View -- 2.1.1 Local Decision Support System -- 2.1.1.1 Handler data -- 2.1.1.2 Business data -- 2.1.1.3 Local decision support system user interface -- 2.1.1.4 nAssistc© -- 2.1.2 Centralized Decision Support System -- 2.1.2.1 Centralized decision support system central database -- 2.1.2.2 Handler interfaces -- 2.1.2.3 Business data -- 2.1.2.4 Centralized decision support system HMI -- 2.1.3 Smart Decision Support System -- 2.1.4 Virtual Power Plant -- 2.1.5 Smart City Database -- 2.1.5.1 Digest component -- 2.1.5.2 Open data API services -- 2.1.5.3 Centralized decision support system database -- 2.1.5.4 LDSS database -- 2.2 Deployment View -- 2.3 Conclusion -- Chapter 3 -- Data Privacy and Confidentiality -- 3.1 Confidentiality -- 3.2 Confidentiality and General Security Requirements -- 3.3 The iURBAN Privacy Challenge -- 3.4 Privacy Enhancing via Transparency -- 3.5 Privacy Enhancing via Differential Privacy -- 3.5.1 Privacy-Enhancing Technologies Based on Privacy Protection -- 3.5.2 Privacy Protection Implementation -- 3.6 Conclusions -- References -- Chapter 4 -- iURBAN CDSS -- 4.1 Introduction -- 4.2 Graphical User Interface -- 4.3 Main GUI Functionalities in Detail -- 4.3.1 User Login -- 4.3.2 Toolbar -- 4.3.3 Management -- 4.3.3.1 Map -- 4.3.4 CityEnergyView -- 4.3.4.1 EnergyView -- 4.3.4.2 Filter Maker -- 4.3.4.3 Graph Container -- 4.3.4.4 Help Area -- 4.3.4.5 Consumption 24H/7D/30D -- 4.3.5 Demand Response Management -- 4.3.5.1 DR program -- 4.3.5.2 Peaks monitoring.

4.3.6 Tariff -- 4.3.6.1 Tariff Plans -- 4.3.6.2 Tariff comparison -- 4.3.7 Diagnostic -- 4.3.7.1 DataFlow Offline -- 4.3.7.2 Hot Water Technical Losses -- 4.3.7.3 Heating Technical Losses -- 4.3.8 Weather Forecast -- 4.3.9 User -- 4.3.10 Configuration -- 4.3.10.1 Console -- 4.3.10.2 Controls -- 4.4 Conclusion -- Chapter 5 -- iURBAN LDSS -- 5.1 Introduction -- 5.2 Graphical User Interface -- 5.2.1 Main Graphical User Interface Functionalities -- 5.3 Conclusion -- Chapter 6 -- Virtual Power Plant -- 6.1 Introduction -- 6.2 Virtual Power Plant in iURBAN -- 6.2.1 smartDSS -- 6.2.2 LDSS -- 6.2.3 CDSS -- 6.2.4 VPP -- 6.3 User Interface -- 6.4 City Models -- 6.5 Modeling Approach -- 6.6 Case Study: Rijeka, Croatia -- 6.6.1 "As is" Scenario -- 6.6.2 "What if"-Scenarios -- 6.6.3 Results -- 6.7 FutureWork -- 6.8 Conclusion -- References -- Chapter 7 -- iURBAN Smart Algorithms -- 7.1 Introduction -- 7.2 "As is" Generation and Consumption Forecasts -- 7.2.1 Introduction -- 7.2.1.1 Random forest -- 7.2.1.2 Artificial neural network -- 7.2.1.3 Fuzzy inductive reasoning -- 7.2.2 AI Generation and Consumption Forecast -- 7.2.2.1 Model generation -- 7.2.2.2 Model and prediction configuration parameters -- 7.2.2.3 Grids and levels -- 7.2.3 Development and Implementation -- 7.2.3.1 Code -- 7.2.3.2 Deployment -- 7.3 Dynamic Tariff Comparison and Demand Response Simulation -- 7.3.1 Functionality -- 7.3.2 Stimulus/Response Sequence -- 7.3.3 User Workflow -- 7.3.4 Calculation Methodology -- 7.3.4.1 Price elasticity background -- 7.3.4.2 Dynamic tariff comparison and demand response formula -- 7.3.5 Assumptions and Limitations -- 7.4 Conclusions -- References -- Chapter 8 -- Solar Thermal Production of Domestic Hot Water in Public Buildings -- 8.1 Introduction -- 8.1.1 The Pilot -- 8.2 Public Solar Prosumers Background -- 8.2.1 Background.

8.2.2 How Is the Energy Management and Monitoring Architecture Established? -- 8.3 Case Study of a Prosuming Kindergarten -- 8.3.1 Introduction -- 8.3.2 What We're Interested in and How Data Can Tell It? -- 8.3.3 What the Results Tell Us for Baseline and Post-retrofit Periods? -- 8.3.3.1 What was happening when no energy efficiency measure was implemented back in 2012? -- 8.3.3.2 What happened when the building was deeply renovated and RES was introduced in 2015? -- 8.3.3.3 So how did EE and RES measures bring change in the kindergarten energy balance? -- 8.3.3.4 What is the overall impact of becoming a prosumer? -- 8.3.4 Discussion -- 8.4 Conclusion -- Chapter 9 -- Business Models -- 9.1 Introduction -- 9.2 Benefit Framework for the Operation of an Energy Management Platform -- 9.2.1 Evaluation Framework -- 9.2.2 Assessment of Benefits for Energy Providers -- 9.3 Business Benefits for Related Use Cases -- 9.3.1 Creation of City Energy View -- 9.3.1.1 Testing and validation in the pilot of Plovdiv -- 9.3.1.2 Testing and validation in the pilot of Rijeka -- 9.3.2 What-if Scenarios -- 9.3.3 Auditing/Billing -- 9.3.4 Technical and Non-technical Losses -- 9.3.4.1 Testing and validation in the pilot of Plovdiv -- 9.3.5 Demand Response -- 9.3.5.1 The model -- 9.3.5.2 Regulatory environment -- 9.3.5.3 No real economic benefit -- 9.3.5.4 Demand response-lessons learnt -- 9.3.6 Variable Tariff Simulation -- 9.3.6.1 The model -- 9.3.6.2 Testing and validation in the pilot of Plovdiv -- 9.3.7 Consultancy Services -- 9.4 Conclusion and Policy Implications -- References -- Index -- About the Editors -- About the Authors -- Back Cover.

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city.

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