Short-term load forecasting of 415V, 11kV and 33kV electrical systems using MLP network

Short-term load forecasting of 415V, 11kV and 33kV electrical systems using MLP network

This research explores a short-term on-line forecasting for load-flow forecasting of three different voltage systems. Upon completing this study, four training algorithm of neural network is used namely Back Propagation (BP), Recursive Prediction Error (RPE), Modified Recursive Prediction Error (MRP...

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Bibliographic Details
Published in:2017 International Conference on Robotics, Automation and Sciences (ICORAS) pp. 1 - 5
Main Authors: Saad, Z., Hazirah, A. J. Nur, Suziana, A., Azhar, M. A. A., Yaacob, Z., Ahmad, F., Yusnita, M. A.
Format: Conference Proceeding
Language:English
Published: IEEE 01-11-2017
Subjects:
Buildings
Forecasting
Load forecasting
Load modeling
Mathematical model
Prediction algorithms
Training
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Summary:This research explores a short-term on-line forecasting for load-flow forecasting of three different voltage systems. Upon completing this study, four training algorithm of neural network is used namely Back Propagation (BP), Recursive Prediction Error (RPE), Modified Recursive Prediction Error (MRPE) and Adaptive Learning Recursive Prediction Error (ALRPE). All these training algorithms are performed by using a structured network called Multilayered Perceptron Network (MLP). An on-line MLP is used to predict the usage of electrical power demand. Non-linear autoregressive moving average with an exogenous input model (NARMAX) is selected to train the network. The analyzed data are collected from some power load usage of 415V, 11kV and 33kV systems at UiTM Pulau Pinang. These data sets are used to compare the performance of MLP with different types of learning algorithm. Experimental results showed that ALRPE training algorithm can further improved the performance of non-linear MLP model in the range of 1.02 dB to 3.224 dB of mean square error (MSE) in the model validation.
DOI:10.1109/ICORAS.2017.8308081