1. M. M. Elgazzar and E. E. Hemayed, "Electrical load forecasting using Hijri causal events," 2016 Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2016, pp. 902-906.

1. Electrical Load Forecasting Using
Hijri Causal Events
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
1

2. Outline
– Introduction
– Objective
– Previous work
– Hijri calander
– Model
– Experimental Results
– Conclusions and future work
2

3. Introduction
– Why Load forecasting is important ?
– Types of load forecasting.
– Machine learning techniques (ANN, SVM).
– Statistical techniques (ARIMA, regression).
– Load forecasting parameters.
– Casual events.
3

4. Objective
– Our goal is to study the effect of Ramadan and
religious holidays to match the consumers
behavior for better forecasting.
4

5. Literature review
– Meteorological parameters and Special days
effect.
– The effect of holidays not only influences special
days, but it also influences the previous and next
days.
5

6. Hijri calendrer
– Two main issues considered in special days:-
• working hours
• people activities
– Besides the effect of the day and its impact on
electric demand, and the temperature.
6

7. The hourly peak load of a day in
Ramadan vs. a normal day
7

8. The daily load profile
8

9. Model
– Two models are applied model 1 using ANN and
model 2 Using ANN with Fourier series to consider
• Monthly and week effects.
• Detecting seasonal pattern.
• The accumulative effect of special days.
– Two different architecture are introduced.
9

10. Residuals
10

11. Model
– The red line represents the forecasting without
considering the effect of casual Hijri events. The
blue line gave better results than the red line.
– The effect of Ramadan (22nd of August to 20th of
Sept.) is very clear near the end of the year.
11

12. Results
Parameter ANN model 1 ANN model 2
RMSE(GW/H) 9.429274 0.9634765
MAE (GW/H) 5.882814 0.6359111
MAPE (%) 0.2660583 0.02835947
12

13. Conclusions
– In this paper, we presented the main Hijri casual
events during the Islamic calendar.
– We applied ANN model before and after including
those Hijri casual events. Using the Hijri calendar
casual events, with ANN model, for load
forecasting reduced the forecasting errors.
13

14. Future work
– Using more recent data.
– Applying on short term load forecasting.
– Using different scenarios.
14

15. Thank you for further questions
mmjazzar@ieee.org
15