
Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm
With Artificial Intelligence Integration in Energy and Other Use Cases
Description
Key Features
- Helps users gain fundamental knowledge in technology infrastructure, including AI, machine learning and fuzzy logic
- Compartmentalizes data knowledge into near-term and long-term forecasting models, with examples involving both renewable and non-renewable energy outcomes
- Advances climate resiliency and helps readers build a business resiliency system for assets
Readership
Energy engineers; electrical engineers; data scientists; environmental engineers; alternative energy researchers
Table of Contents
Part I: Infrastructure Concepts
1. Knowledge is Power
2. A General Approach to Business Resilience System (BRS)
3. Data Warehousing, Data Mining, Data Modeling, and Data Analytics
4. Structured and Unstructured Data Processing
5. Mathematical Modeling Driven Predication
6. Fuzzy Logics: A New Method of Predictions
7. Neural Network Concept
8. Population - Human Growth Driving Ecology
9. Economic Factors
10. Risk Management, Risk Assessment, and Risk Analysis
11. Today’s Fast-Paced Technology
Appendix
A: Pendulum Problem
B: Fluorescence Microscopy
C: Factors Contributed to the Financial Crisis 2008 - 2009
D: Factors contributing to the financial crisis of 2008
E: Forecasting the Future by the OECD
F: The 2025 Global Landscape
G: The World in 2050
H: RiskPart II: The Impact of Energy on Tomorrow’s World
12. Understanding of Energy
13. Economic Impact of Energy
14. Renewable Energy
15. Non-Renewable Energy
16. Nuclear Energy as Non-Renewable Energy Source
17. Energy Storage Technologies and their Role in Renewable Integration
Appendix
A: Fission Nuclear Energy Research and Development Roadmap
B: Thermonuclear Fusion Reaction Driving Electrical Power GenerationPart III: The Mathematical Approach and Modeling
18. Predictive Analytics
19. Engineering Statistics
20. Data and Data Collection Driven Information
21. Statistical Forecasting - Regression and Time Series Analysis
22. Introduction to Forecasting: The Simplest Models
23. Notes on Linear Regression Analysis
24. Principles and Risks of Forecasting
25. Artificial Intelligence Driving Predictive and Forecasting Paradigm
Appendix
A: The Weibull Distribution
B: The Logarithm Transformation
C: Geometric Random Walk Model
D: Random Walk Model
E: Examples of Forecasting Driven by Artificial Intelligence and Machine Learning
F: Examples of Python Programming Driving Artificial Intelligence and Machine LearningPart IV: Python Programming Driven Artificial Intelligence
26. Python Programming Driven Artificial Intelligence
27. Artificial Intelligence, Machine Learning and Deep Learning Driving Big Data
28. Artificial Intelligence, Machine Learning and Deep Learning Use Cases
Appendix
A: Artificial Intelligence and Human Intelligence
B: Deep Learning, Machine Learning Limitations and Flaws
C: Machine Learning Driven an E-Commerce
D: From Business Intelligence to Artificial Intelligence
Product details
- No. of pages: 800
- Language: English
- Copyright: © Academic Press 2022
- Published: June 1, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323951128
About the Authors
Bahman Zohuri
Affiliations and Expertise
Farhang Mossavar Rahmani
Farahnaz Behgounia
Affiliations and Expertise
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