American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: http://www.sciepub.com/journal/ajams 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

Abstract

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.

Keywords:
dynamic forecast neural networks

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