M. M. Eljazzar and E. E. Hemayed, "Feature selection and optimization of artificial neural network for short term load forecasting," 2016 Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2016, pp. 827-831.

1. Feature Selection and Optimization of
Artificial Neural Network for Short Term
Load Forecasting
Elsayed E. Hemayed and Maged M. Eljazzar
Computer Engineering Dept.
Faculty of Engineering
Cairo University, Egypt
mmjazzar@ieee.org
2016 Eighteenth International Middle-East Power Systems Conference (MEPCON)
December 27-29, 2016 - Helwan University, Cairo – Egypt
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2. Outline
– Introduction
– Objective
– Previous work
– Load forecasting factors
– Model
– Experimental Results
– Conclusions and future work
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3. Introduction
– Why Load forecasting is important ?
– Types of load forecasting.
– Machine learning techniques (ANN, SVM).
– Statistical techniques (ARIMA, regression).
– Load forecasting parameters.
– Data sets.
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4. Objective
– Our goal is to assist researchers in their work with a
detailed review of load forecasting parameters
– Besides presenting an overview of load forecasting
techniques in short term load forecasting (STLF) in
different scenarios.
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5. Literature review
– Short term load forecasting factors
• Temperature, Humidity, and Precipitation
• Accumulative effect of sunny days.
• Economic factors (electricity price).
– Short term load forecasting Techniques
• Statistical: ARIMA, Regression analysis.
• Artificial intelligence: ANN, SVM, and fuzzy logic.
• Deep learning.
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6. Load forecasting factors
– Location: the demographic location and the culture of the
country.
– forecasting in the Capital city differs than forecasting in a
small city.
– The impact of human activities
• Daily Resolution: such as Ramadan.
• Monthly Resolution : the urban development
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7. Classification of load forecasts
time Weather Economic Land use Cycle Horizon
VSTLF Optional Optional Optional <1
hour
1 day
STLF Required Optional Optional 1 Day 2 weeks
MTLF Simulated Required Optional 1
month
3 years
LTLF Simulated Simulated Required 1 year 30 years
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8. Load forecasting factors
– In some countries, electricity price varied during the day.
It is cheaper at night than at day.
– Because people tend to use electricity for heat storage
equipment at night and during day, use stored heat for
warming the rooms
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9. Model
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10. Model
– ANN are used to study each individual components
according to their influence on the load forecasting.
– The aim is to study the relationship between input and
peak load
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11. Results
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12. Forecasting errors using each factor
independently with peak load
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Factor
included
MAPE MAE RMSE
--------- 0.9902853 22.30397 33.78119
Temp 0.9277951 20.90214 31.68409
Dew Temp 0.9200192 20.73557 30.05431
Wind 0.9802346 21.96305 33.48497
Humidity 0.9533866 21.51869 31.83082

13. Model
– Model 1 represents the temperature only.
– Model 2 represents temperature and dew temperature.
– Model 3 represents temperature, dew temperature and
wind.
– Model 4 represents temperature, dew temperature and
humidity.
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14. Forecasting errors using each factor
independently with peak load
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Model MAPE MAE RMSE
Model 1 0.9277951 20.90214 31.68409
Model 2 0.2990653 6.835276 10.45197
Model 3 0.2928311 6.741303 10.25782
Model 4 0.2734582 6.231536 9.319102

15. Conclusions
– Load forecasting results always contain certain degree of
variance. This variance due to the random Nature of the
load and human behavior.
– The forecasting errors (RMSE, MAPE, MAE) are reduced by
more than half using the hybrid model.
– This work needs to be extended to cover very short term
load forecasting and covers more scenarios;
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16. Thank you for further questions
mmjazzar@ieee.org
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