A Survey on Home Energy Management <s> II. HOME MANAGEMENT SYSTEMS <s> A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home. <s> BIB001 </s> A Survey on Home Energy Management <s> II. HOME MANAGEMENT SYSTEMS <s> Microlocation is the process of locating any entity with a very high accuracy (possibly in centimeters), whereas geofencing is the process of creating a virtual fence around a point of interest (PoI). In this paper, we present an insight into various microlocation enabling technologies, techniques, and services. We also discuss how they can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location-based location aware solutions can play a significant role in facilitating the tenants of an IoT-equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using microlocation enabling technologies. We describe the challenges and propose some potential solutions, such that microlocation enabling technologies and services are thoroughly integrated with IoT-equipped smart building. <s> BIB002 </s> A Survey on Home Energy Management <s> II. HOME MANAGEMENT SYSTEMS <s> Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one. <s> BIB003
Smart buildings represent a branch of ubiquitous computing that comprises the incorporation of Internet of Things (IoT) technologies into buildings for comfort, healthcare, safety, security and energy efficiency BIB001 , BIB002 . They are an integrating part of ongoing technological advancements in power grids, boosting the deployment of smart sensors and other advanced metering devices, which make remote communication, monitoring and actuation on household appliances possible. Among different types of buildings, smart homes have been the subject of great research interest, particularly from the energy efficiency point of view. Smart homes offer better quality of life and efficiency by taking advantage of remote monitoring and self-adaptive context-aware mechanisms, in order to identify needs and preferences of residents, and also to coordinate appliance operation. Wired and wireless sensor and actuator networks are deployed on smart homes, being collected sensor data and contextual information stored in a central platform. This entity is also responsible for processing acquired information, enabling an optimised management and actuation of household appliances, for the sake of residents' comfort and energy efficiency. Distributed power generation has also been boosted, particularly from renewable sources such as hydro, solar and wind  . Furthermore, individual households are also becoming players in the production of their own electricity, via local (micro) solar and wind systems. When power generation exceeds local demand, the resulting surplus can be used to charge local batteries, for subsequent domestic use, or inject into the grid with a given profit. Grid power injection requires a bi-directional interaction between the grid and local microgeneration systems, propped up on a two-way communication network, so as to ensure grid safety and stability. Taking advantage of advanced metering infrastructures (AMI) and remote control and automation systems, grid information can also be considered in managing power resources at household level. This is particularly valuable for utility and grid companies, as it allows them to predict future demands with superior accuracy, reducing electricity waste and decreasing generation costs. From a demand-side perspective, HEMSs are in-line with the smart grid paradigm shift. A HEMS has the ability to interact with household devices and the utility, allowing appliance schedules to be adjusted, in order to cope with constraints and taking into account external information, such as updated grid prices or meteorological forecasts BIB003 . This is usually achieved by turning devices on or off, reducing the overall demand and considering periods of low electricity price and higher power generation. Figure 1 presents the general architecture of a HEMS, which comprises the following components: