Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting

Author:

Yadav Mohini1ORCID,Jamil Majid1,Rizwan Mohammad2,Kapoor Richa3

Affiliation:

1. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, India

2. Department of Electrical Engineering, Delhi Technological University, New Delhi 110042, India

3. Department of Electrical and Electronics, Hindustan College of Science and Technology, Mathura 281122, India

Abstract

Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,General Computer Science,Signal Processing

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