A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications
The rapid growth in electricity demand, driven by its expanding applications across diverse sectors, has emphasized the criticality of maintaining a balanced and reliable power supply. Accurate load forecasting has become a cornerstone of modern power system management, enabling the efficient planni...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525000546 |
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| author | Mahmudul Hasan Zannatul Mifta Sumaiya Janefar Papiya Paromita Roy Pronay Dey Nafisa Atia Salsabil Nahid-Ur-Rahman Chowdhury Omar Farrok |
| author_facet | Mahmudul Hasan Zannatul Mifta Sumaiya Janefar Papiya Paromita Roy Pronay Dey Nafisa Atia Salsabil Nahid-Ur-Rahman Chowdhury Omar Farrok |
| author_sort | Mahmudul Hasan |
| collection | DOAJ |
| description | The rapid growth in electricity demand, driven by its expanding applications across diverse sectors, has emphasized the criticality of maintaining a balanced and reliable power supply. Accurate load forecasting has become a cornerstone of modern power system management, enabling the efficient planning, operation, and design of electrical grids. With the increasing penetration of renewable energy sources and the rise of smart grid technologies, the need for precise forecasting methodologies has intensified to ensure enhanced grid stability, efficiency, and seamless renewable integration. This article systematically reviews contemporary state-of-the-art forecasting techniques, critically analyzing their performance, applications, and outcomes. Emphasis is placed on methodologies for predicting renewable energy availability, electricity pricing, and load demand, with an in-depth evaluation of their modeling frameworks and predictive accuracies. The review highlights significant advancements in artificial intelligence-based approaches, particularly machine learning and neural network models, which consistently outperform traditional forecasting methods in terms of precision and robustness. For enhanced clarity, key insights and comparative analyses are summarized in comprehensive tables, facilitating efficient reference. This review aims to provide researchers with a thorough understanding of advanced forecasting models, their capabilities, and limitations, thereby guiding future research endeavors in the domain of load forecasting. |
| format | Article |
| id | doaj-art-56c8a1608f3e4f9499fc7f2d5834612d |
| institution | OA Journals |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-56c8a1608f3e4f9499fc7f2d5834612d2025-08-20T02:31:56ZengElsevierEnergy Conversion and Management: X2590-17452025-04-012610092210.1016/j.ecmx.2025.100922A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applicationsMahmudul Hasan0Zannatul Mifta1Sumaiya Janefar Papiya2Paromita Roy3Pronay Dey4Nafisa Atia Salsabil5Nahid-Ur-Rahman Chowdhury6Omar Farrok7Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology (RUET), Rajshahi 6204, BangladeshDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, BangladeshDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, Bangladesh; Corresponding author.Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshThe rapid growth in electricity demand, driven by its expanding applications across diverse sectors, has emphasized the criticality of maintaining a balanced and reliable power supply. Accurate load forecasting has become a cornerstone of modern power system management, enabling the efficient planning, operation, and design of electrical grids. With the increasing penetration of renewable energy sources and the rise of smart grid technologies, the need for precise forecasting methodologies has intensified to ensure enhanced grid stability, efficiency, and seamless renewable integration. This article systematically reviews contemporary state-of-the-art forecasting techniques, critically analyzing their performance, applications, and outcomes. Emphasis is placed on methodologies for predicting renewable energy availability, electricity pricing, and load demand, with an in-depth evaluation of their modeling frameworks and predictive accuracies. The review highlights significant advancements in artificial intelligence-based approaches, particularly machine learning and neural network models, which consistently outperform traditional forecasting methods in terms of precision and robustness. For enhanced clarity, key insights and comparative analyses are summarized in comprehensive tables, facilitating efficient reference. This review aims to provide researchers with a thorough understanding of advanced forecasting models, their capabilities, and limitations, thereby guiding future research endeavors in the domain of load forecasting.http://www.sciencedirect.com/science/article/pii/S2590174525000546Load forecastingArtificial intelligence in power systemsRenewable energy integrationSmart grid stabilityMachine learning for energy demand predictionOperation and planning |
| spellingShingle | Mahmudul Hasan Zannatul Mifta Sumaiya Janefar Papiya Paromita Roy Pronay Dey Nafisa Atia Salsabil Nahid-Ur-Rahman Chowdhury Omar Farrok A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications Energy Conversion and Management: X Load forecasting Artificial intelligence in power systems Renewable energy integration Smart grid stability Machine learning for energy demand prediction Operation and planning |
| title | A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications |
| title_full | A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications |
| title_fullStr | A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications |
| title_full_unstemmed | A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications |
| title_short | A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications |
| title_sort | state of the art comparative review of load forecasting methods characteristics perspectives and applications |
| topic | Load forecasting Artificial intelligence in power systems Renewable energy integration Smart grid stability Machine learning for energy demand prediction Operation and planning |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525000546 |
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