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About this book

This book presents the latest research advancements in the operation of smart homes. It comprises new operation techniques including cooperative distributed energy scheduling, framework to react to malicious cyberattacks, framework for demand-side management, and framework for the design of smart homes to support residents’ wellness as well as new optimization techniques such as stochastic model predictive control and multi-time scale optimization. In addition, the book analyzes 11,000 studies that have been indexed in scientific databases and categorizes them based on various data points, including the field and the subject of the research, the name of the institutions, and the nationality of the authors.

Presents new operation techniques of smart homes;Introduces new optimization techniques for operation of smart homes;Analyses 11,000 studies and categorizes them based on different data points.

Table of Contents


Chapter 1. Worldwide Research Trends on Smart Homes

Research in smart homes is still quite recent; however, there is no doubt that it will become a pervasive research topic in the near future. This chapter analyzes the whole research production on smart homes indexed in the Scopus database from 1985 to 2019, yielding a total of 11,000 studies. One of the goals of this chapter is to identify the main countries and institutions that have published on this topic and what their interest has been throughout the time. Four out of 116 countries stand out in this field, namely China, USA, India, and South Korea. In terms of the main institutions, the three with the highest scientific output are Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Another aim of the chapter is to determine the research fields and subfields investigated about smart homes. The publications are mainly focused on two scientific fields, that is, computer science and engineering, accounting for 64% of the overall scientific production. In an aggregate analysis of all publications, four main clusters have been identified, namely Internet of Things, Activity Recognition, Security, and Energy.
Esther Salmerón-Manzano, Mehdi Rahmani-Andebili, Alfredo Alcayde, Francisco Manzano-Agugliaro

Chapter 2. Multi-time Scale Stochastic Model Predictive Control for Cooperative Distributed Energy Scheduling of Smart Homes

In this chapter, a multi-time scale stochastic model predictive control (MPC) approach is applied to solve the cooperative distributed energy scheduling problem of smart homes. In this problem, a variety of energy resources are considered for each smart home, and every smart home has a capability of power transaction with the retailer through the electrical grid as well as with the other connected smart homes. The challenges of the problem include modeling the technical and economic constraints of the energy resources and addressing the variability and uncertainty issues of renewables’ power that change the optimization problem to a stochastic, dynamic (time-varying), and mixed-integer nonlinear programming (MINLP) problem. To deal with the variability and uncertainty issues, a multi-time scale stochastic MPC is applied. Applying the multi-time scale approach in the stochastic MPC is able to simultaneously consider the precise resolution for the problem variables and the vast vision for the optimization time horizon. In addition, linear programming (LP) and genetic algorithm (GA) are combined (GA-LP) and applied in the problem as an effective and fast optimization technique. The numerical studies about the small and large systems demonstrate the competences of the proposed approach.
Mehdi Rahmani-Andebili

Chapter 3. How to Employ Competitive Smart Home Retailers to React to Cyberattacks in Smart Cities?

The growing implementation of information/communication technology accompanied with increasing integration of distributed generation (DG) and smart homes (SHs) have made smart cities more prone to malicious cyberattacks. This chapter proposes a three-layer framework to react to cyberattacks reported by decentralized DG retailers and SH retailers. In the first layer, the system operator administratively modifies the topology of the system to enhance the network reliance on the non-attacked retailers. In the second layer, non-attacked SH retailers are handled by the market operator to manage congestions caused by the administrative action of the system operator in outages prevention. In the third layer, the system operator applies a forced load curtailment on the non-attacked SH retailers who were passive in the second-layer market mechanism to relieve the remaining congestions. The performance of the introduced framework is verified by its implementation on a distribution network modified to contain DG retailers and SH retailers.
Arash Asrari

Chapter 4. Demand Response Frameworks for Smart Residential Buildings

Nowadays, the electrical utilities are concentrating more on smart grid technologies in order to attain reliable, secure and profitable power system operation. Considering various techniques of smart grid, demand side management (DSM) is a promising technique for utility in which the end subscribers are motivated to participate directly in energy society activities. In DSM scheme, the utility proposes various pricing strategies and maximum demand limit (MDL) to get more profit and decrease the operational difficulties. The end subscribers are expected to respond (demand response) appropriately to decrease their electricity bill. Further, the recent advancements and extensive use of smart residential appliances and incorporation of communication and information technologies help consumers to reach minimum electricity bill by altering their demand pattern. Further, the residential consumers prefer battery back-up to reduce their demand during utility peak intervals. In addition to this, residential consumers use renewable power generations as an alternative to meet their demand either completely or partially. Further, they are also stimulated to export their surplus power generation to the grid at utility preferred price. These types of consumers are commonly called as prosumers. Consequently, utilities are introducing a time-dependent power injection limit to avoid grid operational difficulties. In order to attain more incentives from utilities without sacrificing the comfort, the end-user prefers to install building energy management systems. This chapter presents various energy management frameworks for different residential buildings. Further, the presented demand response frameworks are validated through different case studies on a smart residential building equipped with different kinds of household components. The results of the case studies demonstrate considerable yields for the end subscribers.
S. L. Arun, M. P. Selvan

Chapter 5. Smart Homes to Support the Wellness and Pleasurable Experience of Residents

This chapter introduces the design of smart homes to support residents’ wellness and pleasurable experience. Smart homes should contribute to the heathy and happy living of their occupants by incorporating various technologies and devices into a domestic setting. Further, the design of smart homes should provide occupants with pleasurable experiences by the implementation of intuitive interfaces using a user-centered approach. The success of a smart home is dependent on its occupants’ acceptance of and engagement with it in the context of daily living; thus, more specific user studies should be conducted to implement a positive technology to fulfill users’ daily needs. This chapter first identifies the main issues for the design of smart homes by critically reviewing the related research and then frames the crucial factors, emphasizing wellness and pleasurable experience for consideration in the construction of smart homes. A framework for the design of smart homes was developed by focusing on the practicability of each variable from the perspective of supporting user experience. By utilizing the framework, more customized design factors that must be considered in the creation of smart homes could be developed to target user groups with support for their health and happiness.
Mi Jeong Kim, Myung Eun Cho, Han Jong Jun


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