1. Machine Learning Algorithms Predicting Demand Response Potential Utilising a Synthetic Repository Despoina Christantoni, Dimitrios-Stavros Kapetanakis, Donal P. Finn University College of Dublin, Ireland Despoina Christantoni is funded under Programme for Research in Third Level Institutions and co-funded under the European Regional Development Fund (ERDF). Investing in Your Future

2. Context

3. Demand Response Reference: Sonja van Renssen, Nature Climate Change

4. Testbed building 11,100 m 2 floor area Key Features:  Offices, Retail  Fitness Centre  50 m Swimming Pool  Cinema / Theatre  Debating Chamber  Meeting Rooms UCD Sports Centre

5. Testbed building

6. Building Energy Simulation Model  BEMS data archived at 15 minute intervals:  Electricity and gas consumption  Zonal parameters  Electricity: MBE: -1.6% & CVRMSE: 10.5% (Reference: D. Christantoni, S. Oxizidis, D. Flynn, D. P. Finn, Calibration of a commercial building energy simulation model for demand response analysis, in: Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association, 2015, pp. 2865–2872.) EnergyPlus model

7. DR Strategies  Scheduled values changed when a DR signal received  Strategies tested :  Chilled water temperature adjustment  Fans (VAV, CAV & on/off)  Zone air temperature set-point adjustment

8. DR Strategies  Energy Management System  Sensors  Actuators  Parametrics

9. Synthetic Repository  Demand response potential in 15 minutes intervals  1 & 2 hours duration events  Various commencement time  Train and test the machine learning algorithms

10. Machine Learning Algorithms  Artificial neural networks and support vector machines  Focus on: predicting the DR potential with real time weather data

11. Inserts the dataset to the stream Input variables and target variable are selected Model Builder Describes the developed model Predictive model Information about model performance Reports the outcome of prediction Predictive Models Software IBM SPSS Modeler

12. Progress Summary  Synthetic Repository from Testbed Building: Completed  Development of predictive models: Undergoing  Work to be completed: By the end of August

13. Thank you for your attention! Despoina Christantoni: despoina.christantoni@ucdconnect.iedespoina.christantoni@ucdconnect.ie Dimitrios-Stavros Kapetanakis: dimitrios.kapetanakis@ucdconnect.iedimitrios.kapetanakis@ucdconnect.ie