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Über dieses Buch

This book provides a comprehensive introduction to different elements of smart city infrastructure - smart energy, smart water, smart health, and smart transportation - and how they work independently and together. Theoretical development and practical applications are presented, along with related standards, recommended practices, and professional guidelines. Throughout the book, diagrams and case studies are provided that demonstrate the systems presented, and extensive use of scenarios helps readers better grasp how smart grids, the Internet of Things, big data analytics, and trading models can improve road safety, healthcare, smart water management, and a low-carbon economy. A must-read for practicing engineers, consultants, regulators, utility operators, and environmentalists involved in smart city development, the book will also appeal to city planners and designers, as well as upper-level undergraduate and graduate students studying energy, environmental science, technology, economics, signal processing, information science, and power engineering.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Smart City

Abstract
In this chapter characteristics, functionality, and domain of smart city will be explained. Different elements of smart city, such as smart energy, smart health, smart water, smart infrastructure, and big data analytics will be looked at. Case studies will be used to demonstrate the work done in smart city and some benefits derived from the work. Some examples on smart city worldwide will be listed. Challenges and opportunities derived from future smart city will be discussed.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chapter 2. Data Analytics for Solar Energy in Promoting Smart Cities

Abstract
In this chapter, a comprehensive review of the emerging high penetration of photovoltaic (PV) with an overview of electrical energy storage (EES) for PV systems is presented. The emerging cell technologies are discussed. A study of solar power forecasting techniques for operation and planning of PV and EES is included. A deterministic approach for sizing PV and ESS with anaerobic digestion (AD) biogas power plant is developed to achieve a minimal levelized cost of energy (LCOE) for minimizing energy imbalance between generation and demand due to AD generator constraint and high penetration of PV. The issues in correlation analysis due to imbalanced data and data uncertainty in machine learning are given. A proposed framework is developed and tested with an in-depth analysis of real-life solar irradiance and weather condition data. Cluster analysis with Fuzzy C-Means with dynamic time warping and other methods is performed on real-life solar data to determine the clearness index (CI), which could be varied significantly in different seasons.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chapter 3. Blockchain Applications in Microgrid Clusters

Abstract
There is a need for a new market approach to facilitate the power generation and load balance; and make the optimal usage of low carbon energy generation. Based on high security, high transparency, high tamper-proof, and decentralization, blockchain is suitable for microgrids with high renewable penetration and advanced Supervisory Control and Data Acquisition (SCADA) sensors. Microgrids with blockchain can give a more resilient, cost-efficient, low-transmission-loss, and environment-friendly grid. This chapter first presents the motivations for blockchain and peer-to-peer (P2P) energy trading. The technical background including fundamentals of blockchain technology is provided. Second, the chapter gives an overview of blockchain applications specifically for microgrids by reviewing the state-of-the-art. Third, the framework and components of a P2P energy trading model for microgrids and microgrid clusters are examined. Smart contract-based hybrid P2P energy trading model with cryptocurrency named Localized Renewable Energy Certificate (LO-REC) will be discussed. The advantages and challenges of combining blockchain with microgrids are identified. This section serves as a guide for future research on blockchain applications in microgrids.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chapter 4. A Time-Synchronized ZigBee Building Network for Smart Water Management

Abstract
Water management is an important task in economics and the environment. There is a need for developing a scalable, flexible, and reliable sensor network to install and replace water sensors in buildings. Wireless connections will be the first priority. However, inappropriate time synchronization in the network will cause packet loss and long latency that degrades the network performance. In this chapter, time-synchronized ZigBee building network is proposed for water management based on the node-to-node time synchronization. The simulation result shows that the mean synchronization error and variance are low. Also, an interference-mitigated ZigBee-based advanced metering infrastructure solution has been developed for high-traffics smart metering. To evaluate the performance of the network due to interference, the channel-swapping time was investigated. Evaluation results show that there are significant improvements in the performance on the application-layer transmission rate and the average delay.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chapter 5. A Narrowband Internet of Thing-Based Temperature Prediction for Valve-regulated Lead Acid Battery

Abstract
Valve-regulated lead acid (VRLA) battery, sometimes called sealed lead–acid (SLA) or maintenance-free battery, due to its huge market, plays an important role in industries. However, the safety of VRLA has been a wide concern since it is prone to self-heating problems, which generate extra cost or even cause accidents when the internal temperature (IT) of VRLA is exceeded. To prevent potential hazards, effective internal VRLA temperature monitoring methods are required. In this chapter, a narrowband (NB) internet of thing (IoT)-based VRLA battery internal temperature prediction (VBITP) algorithm is developed to provide early warning of battery temperature. In VBITP, the internal temperature is estimated by ambient temperature (AT) and input current (IC) through a pre-trained prediction model. The measured temperature data will be sent to the backend server using NB-IoT. A kind of recurrent neural network, namely, nonlinear autoregressive exogenous (NARX) is applied to determine the potential relationship between the input AT, IC, and the output IT. The experimental results show that VBITP could estimate the IT of VRLA battery with an error rate of 0.04.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Chapter 6. Health Detection Scheme for Drunk Drivers

Abstract
The emerging wireless technologies facilitate network expansion by connecting different kinds of devices and sensors together. The integration of the wearable sensors is usually defined as wireless body network that facilitates real-time monitoring status of the human in numerous applications such as worker safety and patient tracking. It is found that more than 60% of adult drivers felt sleepy while driving and more than 40% of traffic accidents are caused by drunk drivers. In this chapter, an electrocardiogram (ECG) based status of human detection (ECG-HSD) scheme is proposed to detect both drowsy and drunk status. In the ECG-HSD scheme, similarities of ECG signals under normal, drowsy, and drunk conditions are extracted and the corresponding feature vector is built. The important data points on ECG samples are weighted to improve detection accuracy. With a multiple criteria decision-making approach, the results revealed that the ECG-HSD scheme could achieve satisfying accuracy and short testing time.
Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

Backmatter

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