You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Home Energy Management 
Chapter Title:  4) MACHINE LEARNING 
1. Abstract of Cited Paper (BIB001):  Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Literature Review:
Traditionally, utility companies have heavily relied on control systems, physical modelling and numerical calculations to monitor and manage grid infrastructures BIB002 . With the recent technological advances in IoT and smart grid technologies, a new generation of digital sensors equipped with computing and communication capabilities is being deployed. This results in the collection of massive data volumes, which need to be subsequently processed. On the other hand, renewable energy generation increases both the complexity and uncertainty of grid management. As this new set of challenges is not fully addressed by traditional strategies, the application of machine learning and data science-based techniques have been suggested in the context of building and grid management BIB001 , BIB002 , in particular for: (i) appliance scheduling, and (ii) forecasting building energy consumption.