1. A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based on Large Datasets Hai Xiang ZHAO, Phd candidate Frédéric MAGOULÈS, Full Prof. in ECP

2. Outline  Background  SVM theory  Obtain historical data  Data analysis  Experiments and results  Conclusion

3. Background Energy efficiency for buildings. Complex --- many influence factors:  Ambient weather conditions  Building construction and materials  Occupants and their behaviors  Inner facilities ... Approaches:  Engineering, simulation, statistical models...

4. Heat gain: Outside:  Solar radiation  △ T (through walls) Inside:  Electrical plants  Occupants Heat loss: Infiltration Ventilation — Ambient weather conditions — Building construction, materials — Occupants and their behaviors — Inner facilities —... A complex system with many factors involved

5. Background Energy efficiency for buildings. Complex --- many influence factors:  Ambient weather conditions  Building construction and materials  Occupants and their behaviors  Inner facilities ... Approaches:  Engineering, simulation, statistical method...

6. Support vector machine (SVM) Samples: Loss function: Decision function: Maximize: Constraints:,

7. Kernel: Radial Basis Function (RBF) Parallel approach ( D. Brugger 2007 )  Sequential minimal optimization (SMO)  Kernel evaluation  Distributed storage of kernel rows Performance evaluation  Mean squared error (MSE)  Squared correlation coefficient (SCC)

8. Obtaining historical data — Simulation in EnergyPlus LocationParis-Orly, City DurationFrom Nov 1 to Mar 31 Building ShapeRectangle Structure Length:11 Width:10 Ceiling Height:4 North axis: 10 o Walls 1IN Stucco, 0.0253m 8IN Concrete HW, 0.2033m Insulation, 0.0679m Fenestration surface14m 2 for each wall Thermal Zones1 Number of people14 Air infiltration0.0348 m 3 /s Heating typeDistrict heating Cooling type HVAC (windowAirConditioner) Other facilitiesLight, Water heater Table 1: Input parameters for a single building.Fig 1: Flow chat of simulating multiple buildings.

9. Data analysis Fig 2: Flow chat of the data analyzing 5-fold cross validation Analyzing steps – Fig 2 Estimation of ———— Experimental environment: — Two nodes, CPU 8 * 2.5GHz 1333MHz FSB and 4G memory — Gigabit Ethernet

10. Results Fig 4: Measured and predicted district heating demand for the last building in heating season. Fig 3: Dry bulb temperature in the first 20 days of January and July.

11. Fig 5: Running time of the training process using a parallel implementation of SVMs. Table 2: Comparison of parallel and sequential implementations. SVs — The number of Support Vectors MSE — Mean Squared Error SCC — Squared Correlation Coefficient ImplementationsSVsMSESCC Sequential275015.01097e-050.997639 Parallel273825.08532e-050.997571 Fig 6: Comparison of the speedup with a theoretical optimal linear speedup.

12. Conclusion A simulation approach to collect enough historical time series data for multiple buildings A statistical learning method is then applied to predict the energy behavior in a completely new building A parallel implementation of support vector regression with RBF kernel is applied to analyze large amounts of energy consumption data.

13. Thank you very much!