A Survey on Home Energy Management <s> 4) MACHINE LEARNING <s> The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies. <s> BIB001 </s> A Survey on Home Energy Management <s> 4) MACHINE LEARNING <s> 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. <s> BIB002
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.