## 1 Introduction
It has a significant impact on power markets, transactions, and revenues [19]. Scholars have proposed various STLF methods [20; 21; 22; 23; 24; 25], influencing power system performance [26] and resource management in modern grids [27]. STLF helps to evaluate energy trading volumes with the wholesale power market and addresses critical issues in grid management, RES optimization, and planning [28; 29]. However, accurate LF in modern grids is sensitive to equipment changes and load asymmetries and requires extended time spans. Therefore, STLF is essential for modern grid operation and security. Despite extensive efforts to improve the efficiency and economic performance of electric utilities through the development of high-precision LF models, limited research papers have focused on STLF [30]. Previous reviews have emphasized specific sectors and have examined either statistical, artificial intelligence (AI), or machine learning (ML) approaches. Additionally, previous research categorized load forecasting into methodologies or application domains. The evolving domains present challenges when existing methods cannot adapt, while computational advances enable more efficient models, often incorporating artificial neural networks (ANNs). Table in Section 2 provides a comparative analysis of STLF reviews to provide context. In light of previous reviews on STLF, the main contributions of this paper are:  
1. Unlike previous reviews, it uniquely focuses on a comprehensive analysis of STLF methodologies (statistical, intelligent computing, hybrid models), considering multiple sectors to provide a broader perspective while integrating the smart grid perspective. 2. It provides a thorough background on load forecasting, emphasizing its importance, load curve characteristics, influencing factors, and categorization. 3. Addressing the evolving landscape, the paper explores STLF in smart grids, highlighting the role of smart devices and storage systems in improving accuracy for efficient energy management. 4. The study concludes with findings, research gaps, and future perspectives from a detailed literature review. Therefore, the main contribution of this paper is to provide a comprehensive study for researchers to identify the work performed in STLF and further explore the future directions along with the role of STLF in smart grids. It lays the foundation for researchers  
Figure 3: Renewable electricity generation trend 2000–2050 (%). to identify the performance of different methodologies in different operating conditions and applications. Section 2 outlines the methodology for a comprehensive study of STLF. Section 3 discusses the importance of load forecasting, load curve characteristics, influencing factors, and model categorization by time horizon. Section 4 discusses evaluation criteria and outlines those used in this review. Section 5 provides a tabular overview of STLF methodologies. Section 6 provides a study of STLF methodologies in smart grid applications. Section 7 analyzes models based on accuracy, time, and geographic factors. Section 8 highlights significant findings. Section 9 addresses research gaps and future directions. Section 10 concludes the study.