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Electrical Load Forecasting Using Hijri Causal Events
Elsayed 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– Hijri calander– 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.– Casual events.
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Objective
– Our goal is to study the effect of Ramadan and religious holidays to match the consumers behavior for better forecasting.
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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.
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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 [email protected]
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