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:  A. TECHNIQUES 
1. Abstract of Cited Paper (BIB001):  In the development of the electricity grid, Demand Response is a current subject of research for reasons of robustness, efficiency and renewable energy integration. Among the aspects of Demand Response, dynamic pricing is critical, and real-time pricing is a favored way of implementing it. The efficiency of such methods would however rely crucially on the ability and willingness of the end-user to react to varying prices. For this reason, we look at the problem of automatically scheduling the appliances that a user needs to run when the energy price is sent by the provider. We propose a discrete-time formulation for this problem, show how it can be solved by a minimum cut algorithm and compare it with previously proposed techniques. Compared to an exact method requiring a mixed integer programming solver, our approach produces solutions very close to optimal in shorter running times and does not require proprietary software. 
2. Abstract of Cited Paper (BIB002):  Abstract The increasing interest in smart home technologies has created a need for a comprehensive literature survey. This article reviews the goals of a smart home energy management system, along with related definitions, applications, and information about the manufacturing of its components. The challenges associated with smart home energy management systems and possible solutions are examined, and the energy factors that contribute to a customer's electricity bill are discussed. A number of price schemes and the load models needed for solving related scheduling optimization problems are also presented, including a review of the literature related to energy management system scheduling with respect to its control, automation, and communication. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the real-time hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms, which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration. 
5. Abstract of Cited Paper (BIB005):  Over the last few years, the fast-growing energy needs across the world have intensified a central challenge: how to reduce the generation and operation costs in power systems and, in parallel, to minimize the hydrocarbon emissions. Moreover, one-quarter of world's population still lacks access to electricity, as the cost of building conventional power grids is not affordable by third world countries. On the other hand, behind-the-meter (BTM) energy systems offer cost-effective solutions to aforementioned challenges, as they enable end-users to satisfy their energy needs with distributed energy generation and storage technologies. To that end, this paper presents a detailed survey of BTM energy management systems. The paper starts with the classification of the electrical loads with respect to their physical properties, priority ranking, and sizes. Next, the literature on BTM energy management systems is systematically classified into three main categories: technology layer, economic layer, and social layer. The technology layer spans the studies related to power systems including distributed generation and storage technologies, whereas the economic layer shows how economic incentives along with optimization and scheduling techniques are employed to shape the energy consumption. The social layer, on the other hand, presents the recent studies on how to employ social sciences to reduce the energy consumption without requiring any technological upgrades. This paper also provides an overview of the enabling technologies and standards for communication, sensing, and monitoring purposes. In the final part, a case study is provided to illustrate an implementation of the system. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  In this work we monitor and study electrical and water consumptions in a home environment in order to reduce its energy costs. To this end, we apply a population-based optimisation algorithm to determine optimal operation schedules for certain appliances in a domestic environment. We propose a criteria function that assesses the performance of an operation schedule for controllable appliances in the household. An optimisation problem is, then, formulated and solved in order to compute the desired operation schedule that minimises this criteria. Particle Swarm Optimisation (PSO) was the algorithm chosen to solve this problem, mainly because of its simple implementation and relatively low computational burden, allowing it to generate (near-optimal) solutions in a feasible time. A fine-tuning of its parameters was promoted. The best computed solutions for a time horizon of one day revealed not only the ability to take advantage of night periods (when energy is cheaper) but also that a positive balance in the electrical bill can be obtained, benefiting from excess photovoltaic energy being sold to the public grid. 
8. Abstract of Cited Paper (BIB008):  Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand–supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

Literature Review:
In the context of consumption shifting, the choice of a particular scheduling technique involves a number of issues. Scheduling is conducted over a future time horizon, for which household demands and electricity generation cannot be perfectly predicted. As such, adequate and representative consumption profiles are required. In addition, the incorporation of uncertainties for future demands and generation should also be considered. More strongly linked to the optimisation strategy is the process of modelling appliances by a HEMS and the underlying time domain representation. Its discretisation into equal-length slots is widely employed, namely hourly-based slots BIB001 , BIB008 , BIB009 . In some cases, time domain discretisation helps the specification of constraints related for instance to appliance models or comfort parameters, just to name out a few. Such representations, however, lead to a larger number of variables, increasing the corresponding computational burden. In such cases, the underlying problems should be reformulated to reduce the number of variables and constraints  . Continuous time domain representations can also be considered (see e.g. BIB002 - BIB005 ), improving scheduling flexibility, as appliances in this case are not constrained to fixed slots. A wide variety of methods and techniques have been suggested to improve energy usage through load scheduling BIB004 , BIB006 , BIB003 - BIB007 . These methodologies can generically be grouped into five categories: i) mathematical optimisation; ii) heuristic and metaheuristic methods; iii) model-based predictive control; iv) machine learning; v) game theory approaches.