# Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm

### With Artificial Intelligence Integration in Energy and Other Use Cases

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Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigms, Forecasting Energy for Tomorrow’s World with Mathematical Modeling and Python Programming Driven Artificial Intelligence delivers knowledge on key infrastructure topics in both AI technology and energy. Sections lay the groundwork for tomorrow’s computing functionality, starting with how to build a Business Resilience System (BRS), data warehousing, data management, and fuzzy logic. Subsequent chapters dive into the impact of energy on economic development and the environment and mathematical modeling, including energy forecasting and engineering statistics. Energy examples are included for application and learning opportunities. A final section deliver the most advanced content on artificial intelligence with the integration of machine learning and deep learning as a tool to forecast and make energy predictions. The reference covers many introductory programming tools, such as Python, Scikit, TensorFlow and Kera.

## 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

- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- About the authors
- Preface
- Acknowledgment
- Part I. The general infrastructure
- Chapter 1. Knowledge is power
- 1.1. Introduction
- 1.2. History of knowledge
- 1.3. Definition of knowledge
- 1.4. Types of knowledge
- 1.5. What value there is in knowing
- 1.6. Limitation of knowing
- 1.7. Measurability of knowledge
- 1.8. Creation of knowledge
- 1.9. Knowledge resources and power
- 1.10. Knowledge and information as goods
- 1.11. Reasons why knowledge is power
- 1.12. Knowledge is power? Those days are long gone
- 1.13. There is more to “the knowledge is power”
- 1.14. Facilitates decision—making capabilities
- 1.15. Stimulates cultural change and innovation
- 1.16. Bottom line
- 1.17. Summary and conclusion
- Chapter 2. A general approach to business resilience system (BRS)
- 2.1. Introduction
- 2.2. Resilience and stability
- 2.3. Reactive to proactive safety through resilience
- 2.4. Reactive to proactive safety through resilience
- 2.5. Risk atom key concept
- 2.6. Business resilience system features
- 2.7. Summary of the business resilience system
- 2.8. Business resilience system project plan
- 2.9. Summary and conclusion
- Chapter 3. Data warehousing, data mining, data modeling, and data analytics
- 3.1. Introduction
- 3.2. Data warehousing concept
- 3.3. Data mart concept
- 3.4. Data mining concept
- 3.5. Data modeling concept
- 3.6. Data analytics concept
- 3.7. Big data and master data management concepts
- 3.8. Architecture and benefits of MDM and big data
- 3.9. Summary and conclusion
- Chapter 4. Structured and unstructured data processing
- 4.1. Introduction
- 4.2. Master data management versus big data
- 4.3. Big data history and current considerations
- 4.4. Real time data processing and data mining
- 4.5. Improving big data analytics with machine learning-as-a-service
- 4.6. The mathematics of data: graph analytics-as-a-service
- 4.7. Cloud database
- 4.8. Summary and conclusions
- Chapter 5. Mathematical modeling driven predication
- 5.1. Introduction
- 5.2. Mathematics for data-driven modeling—the science of crystal balls
- 5.3. Mathematics of probability
- 5.4. Statistical analysis
- 5.5. Bayesian methods and concepts
- 5.6. Markov chain Monte Carlo methods and concepts
- 5.7. Predictive modeling and analysis
- 5.8. Descriptive, prescriptive-analytics-driven predictive analytic
- 5.9. Predictive versus prescriptive analytics
- 5.10. Big data analytics: descriptive versus predictive versus prescriptive
- 5.11. Diagnostic analytics
- 5.12. Analytical techniques
- 5.13. Summary and conclusion
- Chapter 6. Fuzzy logics: a new method of predictions
- 6.1. Introduction
- 6.2. What is fuzzy logic and how it works
- 6.3. Fuzzy logic and fuzzy sets
- 6.4. The fuzzy logic method
- 6.5. The world's first fuzzy logic controller
- 6.6. The rationale for fuzzy logic
- 6.7. Information processing driven by fuzzy logic
- 6.8. Fuzzy logic system type-1 and type-2
- 6.9. Adaptive neuro-fuzzy interface system
- 6.10. Back Propagation Neural Network
- 6.11. Bayesian network
- 6.12. Fuzzy logic algorithms and neural networking
- 6.13. Summary and conclusion
- Chapter 7. Neural network concept
- 7.1. Introduction
- 7.2. Artificial neural network (ANN)
- 7.3. Back-propagation neural networks
- 7.4. Biological background
- 7.5. Biological neural networks
- 7.6. Learning in a neural network
- 7.7. Fuzzy logic and neural networks
- 7.8. Summary and conclusion
- Chapter 8. Population—human growth driving ecology
- 8.1. Introduction
- 8.2. Population—human growth
- 8.3. What is population
- 8.4. The status of population in the world
- 8.5. Trends—forecasting
- 8.6. Growth rates
- 8.7. How many people can planet earth support?
- 8.8. Criteria to be used in our model
- 8.9. Population structure
- 8.10. Religions
- 8.11. The relationship between population and other critical factors
- 8.12. The impact of climate change on human life
- 8.13. Population and economic growth and development
- 8.14. Population and governance
- 8.15. Population and immigration
- 8.16. Refugees
- 8.17. Urbanization
- 8.18. Summary and conclusion
- Chapter 9. Economic factors
- 9.1. Introduction
- 9.2. What is economics?
- 9.3. Key players
- 9.4. Business cycle
- 9.5. Economics factors
- 9.6. Other economic issues
- 9.7. Summary and conclusion
- Chapter 10. Risk management, risk assessment, and risk analysis
- 10.1. Introduction
- 10.2. Predictive risk intelligence
- 10.3. Cognitive computing applications for risk management
- 10.4. Harnessing product safety and recall analytics
- 10.5. Managing algorithmic risks
- 10.6. The future risk, new game, new rules
- 10.7. Global risk management
- 10.8. Risk sensing: the evolving state of the art
- 10.9. Summary and conclusion
- Chapter 11. Today's fast-paced technology
- 11.1. Introduction
- 11.2. Climate change
- 11.3. Human behavior
- 11.4. Technology definition
- 11.5. The impact of modern technology on human behavior
- 11.6. The impact of technology on climate change
- 11.7. Is there any hope
- 11.8. Summary and conclusion
- Part II. The impact of energy on tomorrow’s world
- Chapter 12. Understanding of energy
- 12.1. Introduction
- 12.2. What is energy?
- 12.3. Moving from one source of energy to another
- 12.4. World energy 2017
- 12.5. United States trends
- 12.6. Global demand growth
- 12.7. Use of energy by different sectors in the United States
- 12.8. Energy use in commercial buildings
- 12.9. Nature of demand for the energy in the future
- 12.10. Summary and conclusion
- Chapter 13. Economic impact of energy
- 13.1. Introduction
- 13.2. Historical relationship between energy and GDP
- 13.3. Factors influence the demand for energy
- 13.4. Energy and climate change
- 13.5. The potential future impacts change in climate on the US energy sector
- 13.6. Summary and conclusion
- Chapter 14. Renewable energy
- 14.1. Introduction
- 14.2. Different types of energy
- 14.3. Definition of the renewable energy
- 14.4. Factors affecting the future renewable energy
- 14.5. Types of renewable energy
- 14.6. Summary and conclusion
- Chapter 15. Nonrenewable energy
- 15.1. Introduction
- 15.2. Types of nonrenewable energy
- 15.3. Coal
- 15.4. Natural gas energy
- 15.5. World of cautious
- Chapter 16. Nuclear energy as nonrenewable energy source
- 16.1. Introduction
- 16.2. Nuclear fission process in a nutshell
- 16.3. Nuclear fusion process in nutshell
- 16.4. Why we need nuclear power plants
- 16.5. Is nuclear energy renewable source of energy
- 16.6. Argument for nuclear as renewable energy
- 16.7. Argument against nuclear as renewable energy
- 16.8. Safety
- 16.9. Conclusion
- Chapter 17. Energy storage technologies and their role in renewable integration
- 17.1. Introduction
- 17.2. The electric grid
- 17.3. Power generation
- 17.4. Transmission and distribution
- 17.5. Load management
- 17.6. Types of storage technology
- 17.7. A battery-inspired strategy for carbon fixation
- 17.8. Saliva-powered battery
- 17.9. Summary
- Part III. The mathematical approach and modeling
- Chapter 18. Predictive analytics
- 18.1. Introduction
- 18.2. Predictive analytics history and current advances
- 18.3. How does predictive analytics work?
- 18.4. Why is predictive analytics important?
- 18.5. Predictive analytics benefits and what are they?
- 18.6. Difference between predictive analytics and traditional analytics
- 18.7. Predictive analytics starts driving an organization
- 18.8. Predictive analytics examples and who's using it?
- 18.9. Predictive modeling and how it works?
- 18.10. Directed acyclic graph (DAG)
- 18.11. Notation
- 18.12. Probability
- 18.13. Distributions
- 18.14. Parameter learning
- 18.15. Online learning
- 18.16. Evidence
- 18.17. Instantiation
- 18.18. Joint probability of a Bayesian network
- 18.19. Distributive law
- 18.20. Bayes theorem utilization
- 18.21. Are Bayesian networks Bayesian?
- 18.22. Inference
- 18.23. Queries
- 18.24. Analysis
- 18.25. Dynamic Bayesian networks
- 18.26. Decision graphs
- 18.27. Decision automation approach
- 18.28. Bayesian statistics-driven data science
- 18.29. The difference between Bayesian statistics and machine learning
- 18.30. What do you need to get started using predictive analytics?
- 18.31. Predictive analytics—common use cases plus case study
- 18.32. Rise of prediction in marketing
- 18.33. Benefits of predictive scoring for marketers
- 18.34. The four common challenges of predictive analytics
- 18.35. Summary
- Chapter 19. Engineering statistics
- 19.1. Introduction
- 19.2. Statistical thinking driving decision-making
- 19.3. Statics driving future forecast
- 19.4. Definition of a linear trend
- 19.5. Expected temperature changes: signal and noise
- 19.6. Deriving trend statistics
- 19.7. Trend uncertainties
- 19.8. Confidence intervals and significance testing
- 19.9. Comparing trends in two datasets
- 19.10. Multiple atmosphere/ocean general circulation model (AOGCM) simulations
- 19.11. Practical versus statistical significance
- 19.12. Climate change and global warming
- Chapter 20. Data and data collection driven information
- 20.1. Introduction
- 20.2. Data versus information
- 20.3. Detrend data driving statistics
- 20.4. Engineering data collection
- 20.5. Exploratory data analysis
- 20.6. Exploratory data analysis versus classical and Bayesian data analysis
- 20.7. Exploratory data analysis versus summary analysis
- 20.8. Exploratory data analysis primary and secondary goals
- 20.9. Summary and conclusion
- Chapter 21. Statistical forecasting—regression and time series analysis
- 21.1. Introduction
- 21.2. Signal versus noise
- 21.3. Risk of forecasting
- 21.4. Introduction to ARIMA: a nonseasonal models
- Chapter 22. Introduction to forecasting: the simplest models
- 22.1. Introduction
- 22.2. Forecasting with the mean model
- 22.3. More rules of thumb for confidence intervals
- 22.4. More about t
- 22.5. Our example continued
- 22.6. Presentation of results
- 22.7 Summary
- Chapter 23. Notes on linear regression analysis
- 23.1. Introduction
- 23.2. Linear regression analysis description
- 23.3. Linear regression and correlation
- 23.4. Correlation and regression to mediocrity
- 23.5. Mathematics of the simple regression model
- 23.6. Introducing R-squared
- 23.7. The standard errors of means and forecasts
- 23.8. Linear regression methods
- 23.9 The listen learned
- Chapter 24. Principles and risks of forecasting
- 24.1. Introduction
- 24.2. Forecasting principles and perspectives
- 24.3. How to move data around
- 24.4. Get to know your data
- 24.5. Inflation adjustment (deflation)
- 24.6. Seasonal adjustment
- 24.7. Stationarity and differencing
- 24.8. Autoregressive integrated moving average (ARIMA) models with regressors
- 24.9. The mathematical structure of ARIMA models
- Chapter 25. Artificial intelligence driving predictive and forecasting paradigm
- 25.1. Introduction
- 25.2. Prediction versus forecasting
- 25.3. Artificial intelligence role
- 25.4. Data analytics role
- 25.5. Predictive analytics role
- 25.6. Predictive analytics and machine learning
- 25.7. Applications of predictive analytics and machine learning
- 25.8. Conclusion
- Part IV. Python programming-driven artificial intelligence
- Chapter 26. Python programming–driven artificial intelligence
- 26.1. Introduction
- 26.2. History of knowledge
- 26.3. Definition of knowledge
- 26.4. Python basics
- 26.5. Python standard and add-on libraries
- 26.6. Python for artificial intelligence, machine learning, and deep learning
- Chapter 27. Artificial intelligence, machine learning, and deep learning driving big data
- 27.1. Introduction
- 27.2. What is machine learning?
- 27.3. What is deep learning?
- 27.4. Short description of artificial intelligence, machine learning, and deep learning
- 27.5. Big data
- 27.6. Natural language processing (NLP)
- 27.7. Cognitive science and cognitive linguistics
- 27.8. Neural networks concepts
- 27.9. Data science and data science platform
- 27.10. Conclusion
- Chapter 28. Artificial intelligence, machine learning, and deep learning use cases
- 28.1. Introduction
- 28.2. Apple stock prediction, using machine learning
- 28.3. Boston housing market analysis
- 28.4. Customer segmentation using K-means clustering a machine learning
- 28.5. Credit card fraud detection
- Appendix A: Pendulum problem
- Appendix B: Fluorescence microscopy
- Appendix C: Factors contributed to the financial crisis 2008–09
- Appendix D: factors contributing to the financial crisis of 2008
- Appendix E: Forecasting the future by the OECD
- Appendix F: The 2025 global landscape
- Appendix G: The world in 2050
- Appendix H: Risk
- Appendix I: Fission nuclear energy research and development roadmap
- Appendix J: Thermonuclear fusion reaction driving electrical power generation
- Appendix K: The Weibull distribution
- Appendix L: The logarithm transformation
- Appendix M: Geometric random walk model
- Appendix N. Appendix N: Random walk model
- Appendix O: Examples of forecasting driven by artificial intelligence and machine learning
- Appendix P: Examples of python programming driving artificial intelligence and machine learning
- Appendix Q: Artificial intelligence and human intelligence
- Appendix R: Deep learning, machine learning limitations and flaws
- Appendix S: Machine learning–driven e-commerce
- Appendix T: From business intelligence to artificial intelligence
- Index

## Product details

- No. of pages: 998
- Language: English
- Copyright: © Academic Press 2022
- Published: June 24, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323951128
- eBook ISBN: 9780323951135

## About the Authors

### Bahman Zohuri

Dr. Bahman Zohuri is currently an Adjunct Professor at Artificial Intelligence Scientist at Golden Gate University, San Francisco California, while he also is Research Associate Professor at Electrical Engineering and Computer Science at University of New Mexico at Albuquerque. He is also consulting through his own consulting company that he stared himself in 1991. He has also been a consultant at Sandia National Laboratory after leaving the United States Navy. Dr. Zohuri earned his Bachelor’s and Master’s degrees in Physics from the University of Illinois and his second Master degree in Mechanical Engineering as well as his Doctorate in Nuclear Engineering from University of New Mexico. He has been awarded three patents and has published more than 40 textbooks and numerous other journal publications.

#### Affiliations and Expertise

Adjunct Professor, Artificial Intelligence Scientist, Golden Gate University, San Francisco, CA; Research Associate Professor, Electrical Engineering and Computer Science, University if New Mexico, Albuquerque, New Mexico, USA

### Farhang Mossavar Rahmani

### Farahnaz Behgounia

Farahnaz Behgounia is presently a graduate student at Golden Gate University at San Francisco, California and in the process of obtaining her Master of Science degree from the school of Business Analytics. She has obtained her Bachlor Degreee (BS) in pure mathematics and have taught the subject at various schools as an instructor. Ms. Behgounia’s present interest is in Artificial Intelligence (AL) and its application in industry along with its sub-component such as Machine Learning (ML) and Deep Leaning (DL). Her recent interest in the subject of AI has directed her into more innovative research in AI and writing various algorithim by utilizing python language for various applications such as E-Commerce,the medical field and others.

#### Affiliations and Expertise

Graduate Student, Golden Gate University at San Francisco, California, USA

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