This paper provides a comprehensive survey of Non-Intrusive Load Monitoring (NILM) systems and their associated methods for disaggregated energy sensing. NILM is an attractive method for energy disaggregation as it can discern devices from aggregated data acquired from a single point of measurement. The goal of NILM is to perform detailed energy sensing and provide information on the breakdown of energy spent, enabling automated energy management systems to profile high energy consuming appliances and devise energy conservation strategies. The paper discusses the two major approaches to Appliance Load Monitoring (ALM): Intrusive Load Monitoring (ILM) and NILM. While ILM is more accurate in measuring appliance-specific energy consumption, it is less practical due to high costs and installation complexity. NILM, on the other hand, is more feasible for large-scale deployments. The paper reviews the state-of-the-art load signatures and disaggregation algorithms used for appliance recognition, highlighting challenges and future research directions. It discusses the general framework of NILM, including data acquisition, feature extraction, load identification, and system training. The paper also presents recent advances in load disaggregation techniques, performance evaluation metrics, and non-traditional appliance features. It compares different learning algorithms for load disaggregation, including supervised and unsupervised methods, and highlights their advantages and disadvantages. The paper concludes with a summary of the current practices and challenges in NILM, emphasizing the need for further research to improve the accuracy and efficiency of NILM systems.This paper provides a comprehensive survey of Non-Intrusive Load Monitoring (NILM) systems and their associated methods for disaggregated energy sensing. NILM is an attractive method for energy disaggregation as it can discern devices from aggregated data acquired from a single point of measurement. The goal of NILM is to perform detailed energy sensing and provide information on the breakdown of energy spent, enabling automated energy management systems to profile high energy consuming appliances and devise energy conservation strategies. The paper discusses the two major approaches to Appliance Load Monitoring (ALM): Intrusive Load Monitoring (ILM) and NILM. While ILM is more accurate in measuring appliance-specific energy consumption, it is less practical due to high costs and installation complexity. NILM, on the other hand, is more feasible for large-scale deployments. The paper reviews the state-of-the-art load signatures and disaggregation algorithms used for appliance recognition, highlighting challenges and future research directions. It discusses the general framework of NILM, including data acquisition, feature extraction, load identification, and system training. The paper also presents recent advances in load disaggregation techniques, performance evaluation metrics, and non-traditional appliance features. It compares different learning algorithms for load disaggregation, including supervised and unsupervised methods, and highlights their advantages and disadvantages. The paper concludes with a summary of the current practices and challenges in NILM, emphasizing the need for further research to improve the accuracy and efficiency of NILM systems.