Short-Term Load Forecasting: A Comprehensive Review and Simulation Study With CNN-LSTM Hybrids Approach

Author:

Ullah Kaleem1ORCID,Ahsan Muhammad2ORCID,Hasanat Syed Muhammad1,Haris Muhammad3ORCID,Yousaf Hamza4ORCID,Raza Syed Faraz2,Tandon Ritesh5,Abid Samain2,Ullah Zahid6ORCID

Affiliation:

1. Centre for Advanced Studies in Energy, University of Engineering and Technology, Peshawar (UET Peshawar), Peshawar, Pakistan

2. Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL, USA

3. Institute for Insight, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA

4. Stetson-Hatcher School of Business, Mercer University, Macon, GA, USA

5. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

6. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

Funder

Politecnico di Milano for providing Open Access within the CRUI CARE Agreement

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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