Electrical Load Forecasting Using Hijri Causal Events

Elsayed E. Hemayed and Maged M. Eljazzar

Computer Engineering Dept.Faculty of EngineeringCairo 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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Outline

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

2

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

Objective

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

4

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

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.

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

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The daily load profile

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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.

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Residuals

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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.

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ResultsParameter 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

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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.

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Future work

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

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Thank you for further questions mmjazzar@ieee.org

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