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Feature Selection and Optimization of Artificial Neural Network for Short Term
Load ForecastingElsayed E. Hemayed and Maged M. Eljazzar
Computer Engineering Dept.Faculty of EngineeringCairo University, Egypt
2016 Eighteenth International Middle-East Power Systems Conference (MEPCON)December 27-29, 2016 - Helwan University, Cairo – Egypt
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Outline– Introduction– Objective– Previous work– Load forecasting factors– Model– Experimental Results– Conclusions and future work
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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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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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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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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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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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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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Model
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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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Results
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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
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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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
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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