Using Dynamic Linear Models and Kalman Filter for modeling and forecasting electricity load in Erbil city

  • Kurdistan Ibrahim Mawlood College of Administration and Economics - Statistics department / Salahaddin University-Erbil
  • Rebaz Othman Yahya M.Sc. in Statistics-Ministry of Higher Education and scientific Research
Keywords: Model, phenomenon, Dynamic Linear Models, Kalman, MAPE and RMSE

Abstract

In this paper we constructed four models for hourly (4*24 models for the 24 hours are 96 models of the day) and daily (4*7 models for the 7 days are 7 models of the week) electricity load using dynamic linear models (DLM) with various parameters. The Bayesian method and Kalman filter where used to estimate the parameters of four load models (Simple DLM, Trend DLM, Trend seasonal DLM and Regression DLM) and one step ahead forecasting.

In order to select the best and most efficient model for estimating and forecasting the electricity load in Erbil City, the four models were compared using (mean absolute error, mean absolute percentage error and root mean square error. The results presented in this paper based on real measured data. R-programming language and Microsoft Excel were used for data analyses. The result shows that Regression-DLM has best estimation and forecasting results compared to other models using the accuracy criteria, and  the Kalman filter algorithm is a well-established technique and is suitable for estimating the parameters of load models that are used in this work as dynamic linear equations that include load signal with uncorrelated Gaussian white noise.

References

References
1. Anderson & Moore, B.G. (1979) Optimal Filtering. Prentice-Hall Publishing, University of Newcastle, New South Wales, Australia.
2. Brockwell & Davis, P., R. (2002) Introduction to Time Series and Forecasting, Second Edition, Springer-Verlag New York, United States of America.
3. Brown, & Hwang, R.P. (2012) Introduction to Random Signals and Applied Kalman Filtering With MATLAB Exercises. Fourth Edition, John Wiley & Sons, United States of America.
4. Candy, J. (2009) Bayesian Signal Processing Classical, Modern, and Particle Filtering Methods. One Edition, John Wiley & Sons, United States of America.
5. Durbin, & Koopman, J.S. (2012) Time Series Analysis by State Space Methods. Second Edition Oxford University publishing, UK.
6. Eubank, R. (2006) A Kalman Filter Primer. CRC Press Publishing, United States of America.
7. Fahad, & Arbab, M. N. (2014) Factor Affecting Short Term Load Forecasting. Journal of Clean Energy Technologies, Vol. 2, No. 4, October 2014.
8. Gentleman, R., Hornik, K., & Parmigiani, G. (2009) Dynamic Linear Models with R. Springer Science &Business Media publishing, New York.
9. Grewal, M. & Andrews, A. (2008) Kalman Filtering Theory and Practice Using MATLAB. Third Edition, John Wiley & Sons Publishing, United States of America.
10. Hong, T. (2010) Short Term Electric Load Forecasting. Raleigh, Operations Research PhD, North Carolina united of American.
11. Mawlood, Kurdistan I. (2008) Bayesian Detection and Kalman Filter estimation of signals in White Gaussian noise Process, PhD theses, university of Salahddin-Erbil.
12. Meng, M., Niu, D., & Sun, W. (2011) Forecasting Monthly Electric Energy Consumption Using Feature Extraction. Journal Energies 2011, 4, 1495-1507; doi: 10.3390/en4101495.
13. Pole, A., West, M. & Harrison, J. (1994) Applied Bayesian Forecasting and Time Series Analysis, Springer Science & Business Media.
14. Prado, R. & West, M. (2010) Time Series Modeling, Computation, and Inference. Chapman & Hall, United States of America United States of America.
15. Prandoni, P. & Vetterli, M. (2008) Signal processing for communications. CRC Publishing, Italy.
16. Rabinovich, Semyon, G. (2010) Evaluating Measurement Accuracy a Practical Approach. Springer Science & Business Media, New York.
17. Rothe, J., Wadhwani, A. & Wadhwani, S. (2009) Short Term Load Forecasting Using Multi Parameter Regression. International Journal of Computer Science and Information Security, Vol. 6, No.2.
18. Schutter, J., Geeter, J., Lefobvre, T. & Bruyninckx, H. (1999) Kalman filters a tutorial. Journal, Vol. 40(4), 538-546.
19. Smith, S. (1999) Digital Signal Processing. Second Edition, California Technical Publishing San Diego, California.
20. Taylor, J., & Buizza, R. (2003) Using Weather Ensemble Predictions in Electricity Demand Forecasting, International Journal of Forecasting, 2003, Vol. 19, pp. 57-70.
21. Taylor, J., Menezes, L., & McSharry, P. (2006) Comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 22(1), 1–16.
22. Wang, S. (2006) Exponential Smoothing for Forecasting and Bayesian Validation of Computer Models. A Thesis Presented to the Academic Faculty, Industrial and Systems Engineering
Georgia Institute of Technology.
23. West, M., & Harrison, J. (1997) Bayesian Forecasting and Dynamic Models. Second Edition, Springer-Verlag New York.
24. Willis, H. (2002) Spatial Electric Load Forecasting. Second Edition, Marcel Dekker Company, United States of American
Published
2018-09-10
How to Cite
1.
Mawlood K, Yahya R. Using Dynamic Linear Models and Kalman Filter for modeling and forecasting electricity load in Erbil city. JAHS [Internet]. 10Sep.2018 [cited 22Jul.2019];22(4):347 -33. Available from: http://zancojournals.su.edu.krd/index.php/JAHS/article/view/1993
Section
Articles