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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Residential Electrical Demand Forecasting in
Very Small Scale
Andrei Marinescu
Distributed Systems Group,
Trinity College, Dublin
18.05.2013

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Current electrical grid system
Generation Transmission Distribution
1000 MW 400 kV 220 kV 110 kV 38 kV
End
User

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Distribution level
• Ensemble of substations,
transformers, medium and
low voltage power lines
• Radial networks towards the
end users

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Challenges of Existing Electrical Grid
• Growing power demand
• Distance of power transmission
• Integration of renewable sources
• Reliability and stability

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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

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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

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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

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Factors involved
Correlation analysis of variables:
• Previous Load
• Temperature
• Humidity

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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)

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Neural Network Structure

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Time series smoothing

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Noise elimination

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Statistical Approaches
• Methods used
– AutoRegressive Model (30 days)
– AutoRegressive Moving Average Model (30 days
– AutoRegressive Integrated Moving Average (30
days, noise integration)

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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

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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%)

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Results over three days
90 h:
230h:

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Current results
90 Houses Forecasting Accuracy over 24 hours
230 Houses Forecasting Accuracy over 24 hours

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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%

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Combined graph
Load(W)
Time (1h step)

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Next steps
• Hybrid
• Pattern change detection and relearning
• Port to GridLAB-D (MG Simulator)
• DWL extension with hybrid – transformer
agent

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Thank you!