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Internet of Things for Smart Buildings

You're reading from   Internet of Things for Smart Buildings Leverage IoT for smarter insights for buildings in the new and built environments

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781804619865
Length 306 pages
Edition 1st Edition
Languages
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Author (1):
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Harry G. Smeenk Harry G. Smeenk
Author Profile Icon Harry G. Smeenk
Harry G. Smeenk
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Applications for Smart Buildings
2. Chapter 1: An Introduction to IoT and Smart Buildings FREE CHAPTER 3. Chapter 2: Smart Building Operations and Controls 4. Chapter 3: First Responders and Building Safety 5. Chapter 4: How to Make Buildings Smarter with Smart Location 6. Chapter 5: Tenant Services and Smart Building Amenities 7. Part 2: Smart Building Architecture
8. Chapter 6: The Smart Building Ecosystem 9. Chapter 7: Smart Building Architecture and Use Cases 10. Chapter 8: Digital Twins – a Virtual Representation 11. Part 3: Building Your Smart Building Stack
12. Chapter 9: Smart Building IoT Stacks and Requirements 13. Chapter 10: Understanding Your Building’s Existing Smart Level and Systems 14. Chapter 11: Technology and Applications 15. Part 4: Building Sustainability for Contribution to Smart Cities
16. Chapter 12: A Roadmap to Your Smart Building Will Require Partners 17. Chapter 13: The Importance of Smart Buildings for Sustainability and the Environment 18. Chapter 14: Smart Buildings Lead to Smart Cities 19. Chapter 15: Smart Buildings on the Bleeding Edge 20. Index 21. Other Books You May Enjoy

Intelligent learning

Throughout this book, we have referenced AI, ML, and deep learning and their impact on smart buildings. In this section, we will discuss each of these in more detail and their expected contribution from smart buildings to unified buildings.

Figure 15.5 – Intelligent learning

Figure 15.5 – Intelligent learning

  • AI: AI is comprised of neural networks, deep learning, and ML, which process data to make predictions and decisions. AI can be loosely defined as machines thinking like humans.
  • Artificial neural networks (ANNs): An ANN, more commonly referred to as a neural network, is modeled after the brain’s biological neurons. Here, the neurons are called nodes, and these nodes are clustered together in layers and operate in parallel. When a node receives a computerized numerical signal, it will process it, and then it signals the other neurons connected to it. This neural reinforcement creates improved recognition of patterns and expertise and enhances...
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