Many industries are aggressively growing their digital infrastructure and with it comes an increased demand on electricity driven by both renewable and non-renewable sources of energy. Energy engineers are quickly learning processing information, such as deep learning and AI, but there is a gap on how to utilize AI technology while maintaining sustainable energy needs and invest in the most efficient decisions for energy companies. Forecasting Energy for Tomorrow’s World with Mathematical Modeling and Python Programming Driven Artificial Intelligence is the first volume in a series that delivers knowledge on key infrastructure in both AI technology and energy, showcasing a scientific method to model and make stronger energy forecasts and decisions.
Structured into four development components, the reference lays the groundwork on tomorrow’s computing functionality starting with how to build a Business Resilience System (BRS). Data warehousing, data management, and fuzzy logic are included. In part II, the authors dive further into the impact of energy on economic development and the environment. Chapters are organized by energy sources with each chapter covering definition, present data, future data, technology, and the advantages and disadvantages for each before rounding out with storage technology. Part III adds a layer of mathematical modeling combined with energy forecasting. Starting with engineering statistics, the reference progresses into various kinds of forecasting and plots, starting with the simplest such as linear regression and then advances into the principles of forecasting. Energy examples are included for application and learning opportunities. Last, Part IV delivers the most advanced content into artificial intelligence with 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, Keras and more to utilize linear and non-linear regression models for the purpose of forecasting. Big data in structured and unstructured processing are included, helping the engineer understand the right information for real-time processing. Packed with examples, Forecasting Energy for Tomorrow’s World with Mathematical Modeling and Python Programming Driven Artificial Intelligence gives today’s energy engineers the knowledge of information to make more trusted decisions, forecast energy needs, and build climate resiliency within their operations.
