Load Forecasting Techniques and Their Applications in Smart Grids

Hany Habbak, Mohamed Mahmoud (), Khaled Metwally, Mostafa M. Fouda and Mohamed I. Ibrahem ()
Additional contact information
Hany Habbak: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt
Mohamed Mahmoud: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Khaled Metwally: Department of Computer Engineering and AI, Military Technical College, Cairo 11766, Egypt
Mostafa M. Fouda: Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
Mohamed I. Ibrahem: Department of Cyber Security Engineering, George Mason University, Fairfax, VA 22030, USA

Energies, 2023, vol. 16, issue 3, 1-33

Abstract: The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF technique is most suitable for specific applications in SGs. The findings indicate that AI-based LF techniques, using ML and neural network (NN) models, have shown the best forecast performance compared to other methods, achieving higher overall root mean squared (RMS) and mean absolute percentage error (MAPE) values.

Keywords: load forecasting; smart grids; machine learning; deep learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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