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.
1. Chapter 22. Introduction to forecasting: the simplest models - p. 723 2. Chapter 23. Notes on linear regression analysis - p. 743 3. Chapter 24. Principles and risks of forecasting - p. 771 4. Chapter 25. Artificial intelligence driving predictive and forecasting paradigm - p. 805 5. Part IV: Python programming-driven artificial intelligence - p. 824 6. Chapter 26. Python programming–driven artificial intelligence - p. 827 7. Chapter 27. Artificial intelligence, machine learning, and deep learning driving big data - p. 837 8. Chapter 28. Artificial intelligence, machine learning, and deep learning use cases - p. 889 9. Appendix A: Pendulum problem - p. e1 10. Appendix B: Fluorescence microscopy - p. e7 11. Appendix C: Factors contributed to the financial crisis 2008–09 - p. e17 12. Appendix D: factors contributing to the financial crisis of 2008 - p. e19 13. Appendix E: Forecasting the future by the OECD - p. e23 14. Appendix F: The 2025 global landscape - p. e27 15. Appendix G: The world in 2050 - p. e29 16. Appendix H: Risk - p. e37 17. Appendix I: Fission nuclear energy research and development roadmap - p. e39 18. Appendix J: Thermonuclear fusion reaction driving electrical power generation - p. e61 19. Appendix K: The Weibull distribution - p. e96 20. Appendix L: The logarithm transformation - p. e99 21. Appendix M: Geometric random walk model - p. e119 22. Appendix N. Appendix N: Random walk model - p. e127 23. Appendix O: Examples of forecasting driven by artificial intelligence and machine learning - p. e131 24. Appendix P: Examples of python programming driving artificial intelligence and machine learning - p. e159 25. Appendix Q: Artificial intelligence and human intelligence - p. e203 26. Appendix R: Deep learning, machine learning limitations and flaws - p. e219 27. Appendix S: Machine learning–driven e-commerce - p. e247 28. Appendix T: From business intelligence to artificial intelligence - p. e258 29. Index - p. 949
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.
1. Chapter 22. Introduction to forecasting: the simplest models - p. 723 2. Chapter 23. Notes on linear regression analysis - p. 743 3. Chapter 24. Principles and risks of forecasting - p. 771 4. Chapter 25. Artificial intelligence driving predictive and forecasting paradigm - p. 805 5. Part IV: Python programming-driven artificial intelligence - p. 824 6. Chapter 26. Python programming–driven artificial intelligence - p. 827 7. Chapter 27. Artificial intelligence, machine learning, and deep learning driving big data - p. 837 8. Chapter 28. Artificial intelligence, machine learning, and deep learning use cases - p. 889 9. Appendix A: Pendulum problem - p. e1 10. Appendix B: Fluorescence microscopy - p. e7 11. Appendix C: Factors contributed to the financial crisis 2008–09 - p. e17 12. Appendix D: factors contributing to the financial crisis of 2008 - p. e19 13. Appendix E: Forecasting the future by the OECD - p. e23 14. Appendix F: The 2025 global landscape - p. e27 15. Appendix G: The world in 2050 - p. e29 16. Appendix H: Risk - p. e37 17. Appendix I: Fission nuclear energy research and development roadmap - p. e39 18. Appendix J: Thermonuclear fusion reaction driving electrical power generation - p. e61 19. Appendix K: The Weibull distribution - p. e96 20. Appendix L: The logarithm transformation - p. e99 21. Appendix M: Geometric random walk model - p. e119 22. Appendix N. Appendix N: Random walk model - p. e127 23. Appendix O: Examples of forecasting driven by artificial intelligence and machine learning - p. e131 24. Appendix P: Examples of python programming driving artificial intelligence and machine learning - p. e159 25. Appendix Q: Artificial intelligence and human intelligence - p. e203 26. Appendix R: Deep learning, machine learning limitations and flaws - p. e219 27. Appendix S: Machine learning–driven e-commerce - p. e247 28. Appendix T: From business intelligence to artificial intelligence - p. e258 29. Index - p. 949
