Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses

Ten Frontier Technologies for International Development

The report finds clear evidence of the potential of frontier technologies to contribute to social, economic and political development gains in a number of ways, by: • Driving innovations in business models, products and processes that provide new goods and services to ‘bottom of the pyramid’ consumers; • Providing the means by which to make better use of existing underutilised household and productive assets; • Catalysing increases in demand, nationally and internationally, which create new industries and markets, leading to macro- and microeconomic growth; and • Changing demand for labour and capital, leading to direct job creation and transformation of the workforce. For all of the potential upsides, potential downsides must also be considered. While it will largely be the private sector that will drive deployment of these technologies, the public sector through national regulation, as well as development financing, will have a major role in mediating the pace and direction of technological change, both to achieve development objectives, and to protect potential losers.As new technologies and digital business models reshape economies and disrupt incumbencies, interest has surged in the potential of novel frontier technologies to also contribute to positive changes in international development and humanitarian contexts. Widespread adoption of new technologies is acknowledged as centrally important to achieving the United Nations Sustainable Development Goals by 2030. But while frontier technologies can rapidly address large-scale economic, social or political challenges, they can also involve the displacement of existing technologies and carry considerable uncertainty and risk. Although there have been significant wins bringing the benefits of new technologies to poor consumers through examples such as mobile money or off-grid solar energy, there are many other areas where the applications may not yet have been developed into viable market solutions, or where opportunities have not yet been taken up in development practice

Sustainable Energy Systems: Efficiency and Optimization

This book explores how the concepts, methods and tools of systemic analyses have been utilised in various contexts, and at different levels, to improve the efficiency and optimisation of sustainable energy systems

Smart home technologies in Europe: a critical review of concepts, benefits, risks and policies

Smart home technologies refer to devices that provide some degree of digitally connected, automated, or enhanced services to building occupants. Smart homes have become central in recent technology and policy discussions about energy efficiency, climate change, and the sustainability of buildings. Nevertheless, do they truly promote sustainability goals? In addition, what sorts of benefits, risks, and policies do they entail? Based on an extensive original dataset involving expert interviews, site visits to retailers, and a comprehensive review of the literature, this study critically examines the promise and peril of smart home technologies. Drawing on original data collected in the United Kingdom, which has access to European markets, the study first examines definitions of smart homes before offering a new classification involving 13 categories of smart technology covering 267 specific options commercially available from 113 companies. It situates these different technology classes alongside six degrees or levels of smartness, from the basic or traditional home to the fully automated and sentient home. It then elaborates on the 13 distinct benefits smart homes offer alongside 17 risks and barriers, before introducing seven policy recommendations from the material. It lastly suggests three areas of future research on the demographics and practices of actual smart home adopters, rethinking the duality of “control,” and looking beyond “homes” towards socio-technical systems, practices, and justice

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

Scalable Data-driven Modeling and Analytics for Smart Buildings

Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches — that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis — applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that au- tomatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to re- port city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs

Interactive visualisation of electricity usage in smart environments

Saving electricity is a trending topic due to the electricity challenges that are being faced globally. Smart environments are environments that are equipped with physical objects, which include computers, sensors, actuators, smartphones, and wearable devices interconnected together through the Internet of Things. The Internet of Things provides a network to achieve communication, and computation abilities to provide individuals with smart services anytime, and anywhere. Rapid developments in information technology have increased the number of smart appliances being used, leading to increased electricity usage. Devices and appliances in Smart Environments continue to consume electricity even when not in use, because of the standby function. The problems arise as the electricity consumption of the standby function accumulates to large amounts. Effective communication through visualisation of the electricity consumption in a Smart Environment provides a viable solution to reducing the consumption of electricity. This research aimed to design and developed a visualisation system that successfully communicates electricity consumption to the user using a variety of visualisation techniques. The Design Science Research Methodology was used to address the research questions and was used to iteratively design and develop an energy usage visualisation system. The visualisation system was created for the Smart Lab at the Nelson Mandela University's Department of Computing Sciences. A usability study was conducted to assess the usability and efficacy of the system. The system was found to be usable and effective in communicating power usage to potential customers, since the participants were able to complete the tasks in a short amount of time. The positive results show that visualisation can aid in communicating electricity usage to customers, resulting in a possible reduction in electricity consumption and improved decision-making.Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Interactive visualisation of electricity usage in smart environments

Saving electricity is a trending topic due to the electricity challenges that are being faced globally. Smart environments are environments that are equipped with physical objects, which include computers, sensors, actuators, smartphones, and wearable devices interconnected together through the Internet of Things. The Internet of Things provides a network to achieve communication, and computation abilities to provide individuals with smart services anytime, and anywhere. Rapid developments in information technology have increased the number of smart appliances being used, leading to increased electricity usage. Devices and appliances in Smart Environments continue to consume electricity even when not in use, because of the standby function. The problems arise as the electricity consumption of the standby function accumulates to large amounts. Effective communication through visualisation of the electricity consumption in a Smart Environment provides a viable solution to reducing the consumption of electricity. This research aimed to design and developed a visualisation system that successfully communicates electricity consumption to the user using a variety of visualisation techniques. The Design Science Research Methodology was used to address the research questions and was used to iteratively design and develop an energy usage visualisation system. The visualisation system was created for the Smart Lab at the Nelson Mandela University's Department of Computing Sciences. A usability study was conducted to assess the usability and efficacy of the system. The system was found to be usable and effective in communicating power usage to potential customers, since the participants were able to complete the tasks in a short amount of time. The positive results show that visualisation can aid in communicating electricity usage to customers, resulting in a possible reduction in electricity consumption and improved decision-making.Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society