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Electrical load forecasting using Hijri causal events
- 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