From Load Forecasting to Demand Response - A Web of Things Use Case
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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.
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From Load Forecasting to Demand Response - A Web of Things Use Case
- 1. 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 KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association www.kit.edu
- 2. C2G: customer as active participant of a Smart Grid Goal: For more predictable and managed demand HUMANS THINGS WEB Load forecasting based on human activity and context analysis in a web-based manner. Technology for Pervasive Computing 2 16.10.2014 Yong Ding et al. @ WoT 2014 AI
- 3. Technology for Pervasive Computing The Context: Load & Activity Duality 3 16.10.2014 Urban Area City Blocks Houses Flats Devices Yong Ding et al. @ WoT 2014 Urban Area Social Group Person Energy Measurement Behavioural Recognition Ground Truth Model Input
- 4. We shouldn‘t we be able to generate other business models in the Web? Technology for Pervasive Computing Dynamic Pricing for Households Residents react to a price signal Money is very generic A lot of information That‘s no fun: Long-term interest? 4 16.10.2014 Yong Ding et al.
- 5. links the mobile games world with the energy system Technology for Pervasive Computing The “Bet and Energy” Idea 5 16.10.2014 Yong Ding et al. @ WoT 2014
- 6. Residents bet on energy consumption of devices and entire households Rich interaction: Use analytics, improve your skills Long-term interest! (given fair chance of winning) Residents can win discounts on their bill and other prizes Technology for Pervasive Computing Goal: Increase predictability of residential demand 6 16.10.2014 Yong Ding et al.
- 7. Technology for Pervasive Computing 7 16.10.2014 Yong Ding et al.
- 8. Technology for Pervasive Computing WoT Forecasting Framework – I 8 16.10.2014 Yong Ding et al. @ WoT 2014
- 9. Technology for Pervasive Computing WoT Forecasting Framework – II Main features Data collection via REST API Domain specific modules 9 16.10.2014 Resource Oriented Namespaces based on Metadata (Id, Type,…) Push-notfiication based on simple push scheme (Long Poll, SSE) Modular persistence support for historical data (OpenTSDB, SQLite) activity recognition load forecasting Guarantee of modularity & flexibility Yong Ding et al. @ WoT 2014
- 10. Technology for Pervasive Computing Integration of Bet and Energy 10 16.10.2014 Yong Ding et al. @ WoT 2014
- 11. Smart Meters Bet Acceptance Utility uses databases to offer bets on regional marketplaces Technology for Pervasive Computing Bet and Energy Design Multilayered web architecture Smart Meter & Mobile App Meters report to regional databases Residents use mobile apps to Monitor consumption Close bets Redeem prizes 11 16.10.2014 Yong Ding et al. @ WoT 2014 Bet Offering Utility Resident
- 12. Summary Activity recognition module Load forecasting module Modular and flexible design for real-time execution and evaluation Technology for Pervasive Computing WoT based forecasting infrastructure Use Case: Bet and Energy web app As a first proof-of-concept application 12 16.10.2014 Yong Ding et al. @ WoT 2014
- 13. Technology for Pervasive Computing Urban Area City Blocks Houses Flats Devices 13 16.10.2014 Yong Ding et al. Urban Area Social Group Person Energy Measurement Behavioural Recognition Ground Truth Model Input
- 14. Technology for Pervasive Computing That’s All 14 16.10.2014 Thank You! Questions? Yong Ding et al. @ WoT 2014
Notes de l'éditeur
- Savings in generation, distribution, storage
- Multilayered web architecture consisting of: Relational database: SQL e.g. MySQL Back end: Java Database abstraction e.g. JPA2/EBean RESTful Web Services e.g. Jersey (JAX-RS) auf Grizzly Front end: HTML/JavaScript (Single-Page) e.g. AngularJS/Backbone.js, Bootstrap, jQuery, ...