From Load Forecasting to Demand Response - A Web of Things Use Case
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KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association www.kit.edu Technology for Pervasive Computing From Load Forecasting to Demand Response - A Web of Things Use Case The 5th International Workshop on the Web of Things (WoT 2014) Yong Ding, Martin A. Neumann, Till Riedel , Michael Beigl, TECO, KIT, Germany Ömer Kehri, CAS Software AG, Germany Geoff Ryder, SAP Palo Alto, USA
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This paper provides a Web of Things use case from a personalized load forecasting service to a gami ed demand response program. Combining real-world measuring applications with web-based applications opens new opportunities to the smart grid. For this purpose, we propose a Web of Things framework for a novel load forecasting process at the appliance level. Firstly, we illustrate the concept design of the Web of Things framework consisting of the sensing infrastructure, the activity recognition and the load forecasting modules. Secondly, we show how we guarantee the modularity and flexibility for implementing all the three modules in a web- based manner. On top of our infrastructure, we propose an extended Web of Things use case by integrating our load forecasting approach into a demand response concept.
Transcript of From Load Forecasting to Demand Response - A Web of Things Use Case
- 1. Technology forPervasive ComputingFrom Load Forecasting to Demand Response- A Web of Things Use CaseThe 5th International Workshop on the Web of Things (WoT 2014)Yong Ding, Martin A. Neumann, Till Riedel, Michael Beigl, TECO, KIT, Germanymer Kehri, CAS Software AG, GermanyGeoff Ryder, SAP Palo Alto, USAKIT University of the State of Baden-Wuerttemberg andNational Research Center of the Helmholtz Association www.kit.edu
2. C2G: customer as active participant of a Smart GridGoal: For more predictable and managed demandHUMANS THINGSWEBLoad forecasting based on human activity andcontext analysis in a web-based manner.Technology forPervasive Computing2 16.10.2014Yong Ding et al. @ WoT 2014AI 3. Technology forPervasive ComputingThe Context: Load & Activity Duality3 16.10.2014Urban AreaCity BlocksHousesFlatsDevicesYong Ding et al. @ WoT 2014Urban AreaSocial GroupPersonEnergyMeasurementBehaviouralRecognitionGround Truth Model Input 4. We shouldnt we be able to generate otherbusiness models in the Web?Technology forPervasive ComputingDynamic Pricing for HouseholdsResidents react to a price signalMoney is very genericA lot of informationThats no fun: Long-term interest?4 16.10.2014Yong Ding et al. 5. links the mobile games world with the energy systemTechnology forPervasive ComputingThe Bet and Energy Idea5 16.10.2014Yong Ding et al. @ WoT 2014 6. Residents bet on energy consumption ofdevices and entire householdsRich interaction: Use analytics, improve yourskillsLong-term interest!(given fair chance of winning)Residents can win discounts on their bill andother prizesTechnology forPervasive ComputingGoal: Increase predictability of residentialdemand6 16.10.2014Yong Ding et al. 7. Technology forPervasive Computing7 16.10.2014 Yong Ding et al. 8. Technology forPervasive ComputingWoT Forecasting Framework I8 16.10.2014Yong Ding et al. @ WoT 2014 9. Technology forPervasive ComputingWoT Forecasting Framework IIMain featuresData collection via REST APIDomain specific modules9 16.10.2014Resource Oriented Namespacesbased on Metadata (Id, Type,)Push-notfiication based on simplepush scheme (Long Poll, SSE)Modular persistence support forhistorical data (OpenTSDB, SQLite)activity recognitionload forecastingGuarantee of modularity & flexibilityYong Ding et al. @ WoT 2014 10. Technology forPervasive ComputingIntegration of Bet and Energy10 16.10.2014Yong Ding et al. @ WoT 2014 11. Smart Meters Bet AcceptanceUtility uses databases to offer bets on regionalmarketplacesTechnology forPervasive ComputingBet and Energy DesignMultilayered web architectureSmart Meter & Mobile AppMeters report to regional databasesResidents use mobile apps toMonitor consumptionClose betsRedeem prizes11 16.10.2014Yong Ding et al. @ WoT 2014Bet OfferingUtilityResident 12. SummaryActivity recognition moduleLoad forecasting moduleModular and flexible design for real-time execution andevaluationTechnology forPervasive ComputingWoT based forecasting infrastructureUse Case: Bet and Energy web appAs a first proof-of-concept application12 16.10.2014Yong Ding et al. @ WoT 2014 13. Technology forPervasive ComputingUrban AreaCity BlocksHousesFlatsDevices13 16.10.2014 Yong Ding et al.Urban AreaSocial GroupPersonEnergyMeasurementBehaviouralRecognitionGround Truth Model Input 14. Technology forPervasive ComputingThats All14 16.10.2014Thank You!Questions?Yong Ding et al. @ WoT 2014
