SE4SG 2013 : Residential Electrical Demand Forecasting in Very Small Scale


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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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  • -particular case for Ireland
  • Medium and low voltage lines mostly short
  • -Increase of electric appliances/ electrification of many devices- EV included-distance is far from users due to the traditional sources of energy- coal, gas, hydro-integration of distributed resources – moving beyond the traditional centralised structure to a dynamic grid with many sources of energy-vital systems like hospitals/air traffic control
  • Smart grid – future electric grid, two way communication, self-monitoring, self-healing-disconnects itself from the main grid in case of blackouts, able to sustain the neighbourhood-low voltage- is close to the loads-distributed renewable sources integration
  • -close to consumers because it makes use of micro sources available in the nearness of the consumers, therefore reducing the power transmission losses-adds to the traditional power plants therefore increasing the total electricity provision-in case one source of energy is lacking other can compensate, or in worst circumstances rely on the storage capability-isolates itself from the main grid if malfunctions/blackouts occur, continuing to provide electricity to the users in islanding mode. Contributes to self-healing of the overall network
  • ARMA– autoregressivemoving average models Box-Jenkins Time seriesExp smoothing – exponentially decreasing weights over time based on prev observationsHybrids (usually a combination of two techniques from above)
  • SE4SG 2013 : Residential Electrical Demand Forecasting in Very Small Scale

    1. 1. Lero© 2012Residential Electrical Demand Forecasting inVery Small ScaleAndrei MarinescuDistributed Systems Group,Trinity College, Dublin18.05.2013
    2. 2. Lero© 2012Current electrical grid systemGeneration Transmission Distribution1000 MW 400 kV 220 kV 110 kV 38 kVEndUser
    3. 3. Lero© 2012Distribution level• Ensemble of substations,transformers, medium andlow voltage power lines• Radial networks towards theend users
    4. 4. Lero© 2012Challenges of Existing Electrical Grid• Growing power demand• Distance of power transmission• Integration of renewable sources• Reliability and stability
    5. 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. 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. 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. 8. Lero© 2012Factors involvedCorrelation analysis of variables:• Previous Load• Temperature• Humidity
    9. 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. 10. Lero© 2012Neural Network Structure
    11. 11. Lero© 2012Time series smoothing
    12. 12. Lero© 2012Noise elimination
    13. 13. Lero© 2012Statistical Approaches• Methods used– AutoRegressive Model (30 days)– AutoRegressive Moving Average Model (30 days– AutoRegressive Integrated Moving Average (30days, noise integration)
    14. 14. Lero© 2012Fuzzy ApproachPrevious dayLoadNext dayTemperatureNext dayHumidityNext dayLoad7*24 hoursdifferencebetweeninput andoutput,samples foreach hour
    15. 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. 16. Lero© 2012Results over three days90 h:230h:
    17. 17. Lero© 2012Current results90 Houses Forecasting Accuracy over 24 hours230 Houses Forecasting Accuracy over 24 hours
    18. 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. 19. Lero© 2012Combined graphLoad(W)Time (1h step)
    20. 20. Lero© 2012Next steps• Hybrid• Pattern change detection and relearning• Port to GridLAB-D (MG Simulator)• DWL extension with hybrid – transformeragent
    21. 21. Lero© 2012Thank you!