Hyperparameter optimization of Bayesian neural network using Bayesian optimization and intelligent feature engineering for load forecasting

Zulfiqar, M., Gamage, K. A.A. , Kamran, M. and Rasheed, M. B. (2022) Hyperparameter optimization of Bayesian neural network using Bayesian optimization and intelligent feature engineering for load forecasting. Sensors, 22(12), 4446. (doi: 10.3390/s22124446) (PMID:35746227) (PMCID:PMC9231108)

Abstract

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).

• Item Type: Articles
• Additional Information: This project has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska–Curie grant agreement, No. 754382, GOT ENERGY TALENT.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gamage, Professor Kelum

Creator Roles

Gamage, K. A.A.: Methodology, Validation, Investigation, Resources, Writing – original draft, Writing – review and editing, Project administration, Funding acquisition

• Authors: Zulfiqar, M., Gamage, K. A.A., Kamran, M., and Rasheed, M. B.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 12 June 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Sensors 22(12): 4446
• Publisher Policy: Reproduced under a Creative Commons License