Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

Fanidhar Dewangan, Almoataz Y. Abdelaziz () and Monalisa Biswal
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Fanidhar Dewangan: Department of Electrical Engineering, National Institute of Technology Raipur, Chhattisgarh 492010, India
Almoataz Y. Abdelaziz: Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt
Monalisa Biswal: Department of Electrical Engineering, National Institute of Technology Raipur, Chhattisgarh 492010, India

Energies, 2023, vol. 16, issue 3, 1-55

Abstract: The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper.

Keywords: smart grid; smart sensors; load forecasting (LF); regression; time series; back propagation; recurrent neural network (RNN); long short-term memory (LSTM) (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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