1. Parallel seasonal approach for electrical load forecasting. Presented by: Oussama Ahmia Authors : Oussama Ahmia & Nadir Farah ITISE2015 ITISE 2015, Granada, Spain, 1-3 July

2. Content: 1. Introduction. 2. The electric load. 3. Data Analysis. 4. Load profils. 5. Conception. 6.Results and comments. 7. Conclusion. 1

3. Introduction : - An Important production factor in the modern economical activity. Electrical energy Parallel approach Each season is forecast separately. Multi-model approach 2

4. The electric load. Tendency electrical load graph and its tendency 3

5. The electric load. Saisonality S2 S1 S3 S1 S4 Load values for the year 2012 4

6. Data analysis Introduction Why ? -Better understand the Algerian electricity consumption habits -Construct the load profiles based on the data analysis results Monthly daily -discover the similarities between the different months -discover the consumption seasons -discover the similarities between the different weekdays -discover the weekday types 5

7. Data analysis Used algorithms and variables SOM (kohonen) Hierarchical classifier K-means -The weekly load peak - The monthly peak - The monthly mean Daily analysis Monthly analysis - The percentage of each hour of the same weekday, in relation to the daily peak 6

8. Data analysis Results Monthly analysis C1C2C3C4 JuneMarchJanuaryFebruary novemberAprilJulyseptember MayAugust octoberdécember Results of SOM for the year 2007 C1C2C3C4 JunaryAprilMarchJuly FebruaryMayoctoberAugust Junenovember september décember Results of K-mean for the year 2012 Daily analysis ObservationClasse SUN1 MON1 TUE1 WED1 THU2 FRI2 SAT3 Results of K-mean the year 2007 ObservationClasse SUN1 MON2 TUE2 WED2 THU2 FRI3 SAT3 Results of SOM for the year 2012 7

9. Load profiles Daily PROFILE Weekly PROFILE Annual PROFIL Maximal value of each week maximal load values of each weekday Average load valus for each hour during the day 8

10. Conception Used variables historical values different combinations of historical values are tested and compared: -The electric load values of the same month of the previous years (PMY) -The electric load values of the previous months (PM) GDP 9

11. Conception Used algorithms SVM KERNEL Polynomial RBF PUK (Pearson 7) MLP Linear regression Grid search 10

12. Conception Parallel approch 11 Diagram illustrating an example of prediction of monthly load for the year 2012.

13. Conception From long to short term Global load profile Global profile Annual peak 12

14. Conception Results linear regressionPUKRBFPolyMLP 2 PMY+ 2 PM2,96%3,28%3,15%2,98%3,52% 2 PMY+ PM3,02%3,17%3,23%3,20%3,74% 2 PMY3,60%3,57%3,56%3,64%3,90% 3 PMY3,55%3,50%3,37%3,42%3,86% 4 PMY+ 2 PM2,96%3,02%3,04%3,01%3,75% 4 PMY+ 3 PM2,98%3,12%3,11% 3,93% 4 PMY+ PM3,03%3,12%3,13%3,23%4,16% 4 PMY3,15%3,13%3,10%3,01%3,77% linear regressionPUKRBFPolyMLP 2 PMY+ 2 PM2,94%3,21%2,97%2,72%5,48% 2 PMY+ PM2,83%3,00%2,76%2,73%4,81% 2 PMY3,63%4,33%3,58%3,48%3,71% 3 PMY3,51%3,81%3,05%3,48%3,51% 4 PMY+ 2 PM2,85%2,77% 2,67%4,81% 4 PMY+ 3 PM2,93%2,89%2,88%2,65%3,99% 4 PMY+ PM2,81%2,58%2,48%2,54%4,63% 4 PMY2,97%2,88%2,66%2,79%4,02% linear regressionPUKRBFPolyMLP 2 PMY+ 2 PM2,22%2,10%2,14%2,08%3,77% 2 PMY+ PM2,21%2,37%2,17%2,30%4,04% 2 PMY3,51%3,36%2,98%3,43%2,81% 3 PMY3,45%3,10%2,62%3,16%3,08% 4 PMY+ 2 PM2,35%1,93%1,60%1,99%3,04% 4 PMY+ 3 PM2,53%1,91%1,59%2,20%2,68% 4 PMY+ PM2,38%1,92%1,78%1,97%2,73% 4 PMY2,74%2,44%2,34%2,84%3,76% error rate of the serial approach error rate of the parallel approach error rate of the parallel approach with GDP 13

15. Conception Results ModeleMAPE SVM (polynomial kernel)8.22% SVM (RBF kernel)2.82% SVM (PUK kernel)3.06% Multiple linear regression11.39% MLP3.69% errors rates for the years 2011 and 2012 Prediction of the load, using SVM (RBF kernel), linear regression and MLP from 2013 to 2020. 14

16. Conclusion: GDP is strongly correlated with the electricity consumption. Parallel seasonal prediction, significantly increases the accuracy of the forcast. the use of load profiles allows us to move a load value from the long term to short term. the use of hyperparameters optimisatoin algorithms on SVM and Kernel parameters, increases the precison of the forecast, allowing it to surpass the artificial neural network. 15