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. Load forecasting is the process of predicting how much electricity will be needed at a given time and how that demand will affect the utility grid. It is used to ensure that enough power is available to meet consumption needs while avoiding waste and inefficiency. Electric load forecasting is key. This work was authored, in part, by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U. Department of Energy (DOE) under Contract No. Funding provided by the United States Agency for International Development (USAID). Electricity end use in the United States from 1975 to 2022 Source: © Statista 2023 3 Econometric modeling using historical data (typically load, weather) is not sufficient to forecast future load • Customers are adopting new technologies behind-the-meter • Need to understand gross load. Load forecasting is a critical component of power systems engineering, enabling utilities and grid operators to predict electricity demand and manage energy resources effectively. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques. 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.