Farhang Mossavar Rahmani – författare

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.

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

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.- 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