Title

Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

Document Type

Journal Article

Publisher

Elsevier Ltd

School

School of Engineering

RAS ID

20115

Comments

Originally published as: Mahmoud, T.S., Habibi, D., Hassan, M.Y., & Bass, O. (2015). Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks. Energy Conversion and Managment, 106, 1396-1408. Original article available here.

Abstract

This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

DOI

10.1016/j.enconman.2015.10.066

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