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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SE4SG 2013 : Residential Electrical Demand Forecasting in Very Small Scale
- 1. Lero© 2012 Residential Electrical Demand Forecasting in Very Small Scale Andrei Marinescu Distributed Systems Group, Trinity College, Dublin 18.05.2013
- 2. Lero© 2012 Current electrical grid system Generation Transmission Distribution 1000 MW 400 kV 220 kV 110 kV 38 kV End User
- 3. Lero© 2012 Distribution level • Ensemble of substations, transformers, medium and low voltage power lines • Radial networks towards the end users
- 4. Lero© 2012 Challenges of Existing Electrical Grid • Growing power demand • Distance of power transmission • Integration of renewable sources • Reliability and stability
- 5. Lero© 2012 Microgrids • Part of the structure that defines a smart grid • Can function interconnected to the main grid or in islanded mode • Is a system that operates in low voltage • Composed of micro sources of electricity, mostly renewable
- 6. Lero© 2012 Meeting 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 of blackouts • Minimize electricity price, network overload
- 7. Lero© 2012 Forecasting Day-ahead forecasting: An essential component of the (micro)grid Implemented 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© 2012 Factors involved Correlation analysis of variables: • Previous Load • Temperature • Humidity
- 9. Lero© 2012 Evaluation • Neural networks (FANN opensource C++ toolbox) • Statistical regression for samples • Neuro-fuzzy approch using Matlab’s toolbox • Smoothed load with signal processing and noise elimination for forecasting (Wavelet toolbox)
- 10. Lero© 2012 Neural Network Structure
- 11. Lero© 2012 Time series smoothing
- 13. Lero© 2012 Statistical Approaches • Methods used – AutoRegressive Model (30 days) – AutoRegressive Moving Average Model (30 days – AutoRegressive Integrated Moving Average (30 days, noise integration)
- 14. Lero© 2012 Fuzzy Approach Previous day Load Next day Temperature Next day Humidity Next day Load 7*24 hours difference between input and output, samples for each hour
- 15. Lero© 2012 Evaluation Scenarios • 90/230 houses real load data from Ireland chosen – CER survey • Samples over 426 days (2009-2010) • Split into sets – weekdays/weekend/holidays – training (70%), validation (20%), testing (10%)
- 16. Lero© 2012 Results over three days 90 h: 230h:
- 17. Lero© 2012 Current results 90 Houses Forecasting Accuracy over 24 hours 230 Houses Forecasting Accuracy over 24 hours
- 18. Lero© 2012 Results Method NRMSE 90h 230h ANN 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© 2012 Combined graph Load(W) Time (1h step)
- 20. Lero© 2012 Next steps • Hybrid • Pattern change detection and relearning • Port to GridLAB-D (MG Simulator) • DWL extension with hybrid – transformer agent
Hinweis der Redaktion
- -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)