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

K.A.D. Deshani^1, , 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

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

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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