American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2019, 7(1), 9-17
DOI: 10.12691/ajams-7-1-2
Open AccessArticle

Artificial Neural Network for Dynamic Iterative Forecasting: Forecasting Hourly Electricity Demand

K.A.D. Deshani1, , Liwan Liyanage-Hansen2 and Dilhari Attygalle1

1Department of Statistics, University of Colombo, Colombo 03, Sri Lanka

2School of Computing, Engineering and Mathematics, University of Western Sydney, Campbelltown, Australia

Pub. Date: December 25, 2018

Cite this paper:
K.A.D. Deshani, Liwan Liyanage-Hansen and Dilhari Attygalle. Artificial Neural Network for Dynamic Iterative Forecasting: Forecasting Hourly Electricity Demand. American Journal of Applied Mathematics and Statistics. 2019; 7(1):9-17. doi: 10.12691/ajams-7-1-2


This paper presents the procedure of building a dynamic predictive model using an artificial neural network to perform an iterative forecast. An algorithm is proposed and named as “Artificial Neural Network Approach for Dynamic Iterative Forecasting”. The development of this algorithm focused on feature selection, identification of best network architecture for the model, moving window selection and finally the iterative prediction. This proposed algorithm was deployed to forecast next day’s hourly total demand in Sri Lanka as an illustration. Inclusion of a clustering effect that were based on the specialty of the day, as an input was investigated through this application, from which improved accuracies were shown.

dynamic forecast neural networks

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[1]  Turner, L., & Witt, S. (2001). Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models. Tourism Economics, 7(2), 135-147.
[2]  Prasad, A., Chai, L., Singh, R., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 26-33.
[3]  Snyder, R. (2002). Forecasting sales of slow and fast moving inventories. European Journal of Operational Research, 140(3), 684-699.
[4]  Taylor, J. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 799-805.
[5]  Guan, C., Luj, P., Michel, L., Wang, Y., & Friedland, P. (2013). Very Short-Term Load Forecasting: Wavelet Neural Networks with Data Pre-Filtering. Power Systems IEEE Transactions on, 28(1), 30-41.
[6]  Xie, J., Cheng, C., Chau, K., & Pie, Y. (2006). A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. International Journal of Environment and Pollution, 28(3/4), 364-381.
[7]  Jiang X., Adeli H. (2005) Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting. Journal of Transportation Engineering, 131(10).
[8]  An, N., & Anh, D. (2015). Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network. International Conference on Advanced Computing and Applications. Ho Chi Minh City, Vietnam: IEEE.
[9]  Boné, R., & Crucianu, M. (2009). Multi-step-ahead prediction with neural networks: A review.
[10]  Zhang, G., Patuwo, B., & Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 35(62), 35-62.
[11]  Park, D., El-Sharkawi, M., Marks II, R., Atlas, L., & Damborg, M. (1991). Electric Load Forecasting Using An Artificial Neural Network. IEEE Transadions on Power Systems, 6(2), 442-449.
[12]  Othman, M. M., Harun, M. H., Salim, N. A., & Othman, M. L. (2015). Sequential Process of Feature Extraction Methods for Artificial Neural Network in Short Term Load Forecastng. ARPN Journal of Engineering and Applied Sciences, 10(19), 8830-8838.
[13]  Mandal, P., Senjyu, T., Urasaki, N., & Funabashi, T. (2006). A neural network based several-hour-ahead electric load forecasting using similar days approach. Electrical Power and Energy Systems, 28, 367-373.
[14]  Rana, M., Koprinska, I., & Troncoso, A. (2014). Forecasting Hourly Electricity Load Profile Using Neural Networks. Internationall Joint Conference on Neural Networks, (pp. 824-831). Beijing
[15]  Mishra, D. K., Dwivedi, A., & Tripathi, S. (2012). Efficient Algorithm for Load Forecasting in Electric Power System using Artificial Neural Network. International Journal of Latest Research in Science and Technology, 1(3), 254-258.
[16]  Deshani, K., Attygalle, M., Hansen, L., & Karunarathne, A. (2014, May). An Exploratory Analysis on Half-Hourly Electricity Load Patterns Leading to Higher Performances in Neural Network Predictions. Interanational Journal of Artificial Intelligence and Applications, 5(3), 37-51.
[17]  Deshani, K., Hansen, L., Attygalle M.D.T, & Karunarathne, A. (2014). Improved Neural Network Prediction Performances of Electicity Demand: Modifying Inputs through Clustering. Second Internatioanl Conference on Computational Science and Engineering (pp. 137-147). AIRCC.
[18]  Filik, U. B., Gerek, O. N., & Kurban, M. (2011). Hourly Forecasting of Long Term Electric Energy Demand using Novel Mathematical Models and Neural Networks. International Journal of Innovative Computing. Information and Control, 7(6), 3545-5357.
[19]  Lee, K., Cha, Y., & Park, J. (1992). Short Term Load Forecasting using Artificial Neural Networks. Transactions on Power Systems, 124-132.
[20]  Lu, C. N., & Wu, H. T. (1993). NEURAL NETWORK BASED SHORT TERM LOAD FORECASTING. IEEE Transactions on Power Systems, 336-342.
[21]  Baliyana, A., Gauravb, K., & Mishra, S. K. (2015). A Review of Short Term Load Forecasting using Artificial Neural Network Models. Procedia Computer Science, (pp. 121-125). Odisha.
[22]  McElroy, T. (2015). When are Direct Multi-step and Iterative Forecasts Identical? Journal of Forecasting, 34, 315-336
[23]  Demuth, H., & Beale, M. (2002). Neural Network Toolbox User's Guide - Version 4. Natick: The MathWorks.