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SE4SG 2013 Presentation by Andrei Marinescu ...

SE4SG 2013 Presentation by Andrei Marinescu

at 2nd International Workshop on Software Engineering Challenges for the Smart Grid.

Please cite our workshop at

Ian Gorton, Yan Liu, Heiko Koziolek, Anne Koziolek, and Mazeiar Salehie. 2013. 2nd international workshop on software engineering challenges for the smart grid (SE4SG 2013). In Proceedings of the 2013 International Conference on Software Engineering (ICSE '13). IEEE Press, Piscataway, NJ, USA, 1553-1554.

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- 1. Lero© 2012Residential Electrical Demand Forecasting inVery Small ScaleAndrei MarinescuDistributed Systems Group,Trinity College, Dublin18.05.2013
- 2. Lero© 2012Current electrical grid systemGeneration Transmission Distribution1000 MW 400 kV 220 kV 110 kV 38 kVEndUser
- 3. Lero© 2012Distribution level• Ensemble of substations,transformers, medium andlow voltage power lines• Radial networks towards theend users
- 4. Lero© 2012Challenges of Existing Electrical Grid• Growing power demand• Distance of power transmission• Integration of renewable sources• Reliability and stability
- 5. Lero© 2012Microgrids• Part of the structure that defines a smart grid• Can function interconnected to the main grid orin islanded mode• Is a system that operates inlow voltage• Composed of microsources of electricity,mostly renewable
- 6. Lero© 2012Meeting the demands• Close to consumers• Makes use of additional power sources,including renewables• Is based on several complementary sources• Can match supply with demand• Can function autonomously despite ofblackouts• Minimize electricity price, network overload
- 7. Lero© 2012ForecastingDay-ahead forecasting:An essential component of the (micro)gridImplemented on larger scale with:– Multiple Regression, ARMA (statistical approaches)– Neural networks (including wavelet approaches)– Fuzzy logic– Double Seasonal Holt-Winters Exponential Smoothing(week and day)– Hybrids of the above
- 8. Lero© 2012Factors involvedCorrelation analysis of variables:• Previous Load• Temperature• Humidity
- 9. Lero© 2012Evaluation• Neural networks (FANN opensource C++toolbox)• Statistical regression for samples• Neuro-fuzzy approch using Matlab’s toolbox• Smoothed load with signal processing andnoise elimination for forecasting (Wavelettoolbox)
- 10. Lero© 2012Neural Network Structure
- 11. Lero© 2012Time series smoothing
- 12. Lero© 2012Noise elimination
- 13. Lero© 2012Statistical Approaches• Methods used– AutoRegressive Model (30 days)– AutoRegressive Moving Average Model (30 days– AutoRegressive Integrated Moving Average (30days, noise integration)
- 14. Lero© 2012Fuzzy ApproachPrevious dayLoadNext dayTemperatureNext dayHumidityNext dayLoad7*24 hoursdifferencebetweeninput andoutput,samples foreach hour
- 15. Lero© 2012Evaluation Scenarios• 90/230 houses real load data from Irelandchosen – CER survey• Samples over 426 days (2009-2010)• Split into sets– weekdays/weekend/holidays– training (70%), validation (20%), testing (10%)
- 16. Lero© 2012Results over three days90 h:230h:
- 17. Lero© 2012Current results90 Houses Forecasting Accuracy over 24 hours230 Houses Forecasting Accuracy over 24 hours
- 18. Lero© 2012ResultsMethod NRMSE 90h 230hANN 3.82% 3.05%AR 3.67% 3.05%ARMA 3.61% 2.94%ARIMA 3.63% 2.93%Neuro-Fuzzy 4.28% 3.44%Smoothed 3.84% 3.20%
- 19. Lero© 2012Combined graphLoad(W)Time (1h step)
- 20. Lero© 2012Next steps• Hybrid• Pattern change detection and relearning• Port to GridLAB-D (MG Simulator)• DWL extension with hybrid – transformeragent
- 21. Lero© 2012Thank you!

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