This book describes the practical application of artificial intelligence (AI) methods using time series data in system control. This book consistently discusses the application of machine learning to the analysis and modelling of time series data of physical quantities to be controlled in the field of system control.
Since dynamic systems are not stable steady states but changing transient states, the changing transient states depend on the state history before the change. In other words, it is essential to predict the change from the present to the future based on the time history of each variable in the target system, and to manipulate the system to achieve the desired change.
In short, time series is the key to the application of AI machine learning to system control. This is the philosophy of this book: "time series data" + "AI machine learning" = "new practical control methods".
This book can give my helps to undergradate or graduate students, institute researchers and senior engineers whose scientific background are engineering, mathematics, physics and other natural sciences.
Introduction.- Linear Time Series Modeling.- Deep Learning AI Modeling.- LSTM AI Modeling.- Optimal Control by Time-Series AI Model.- The Reality of Time Series Learning Data Collection.- Practical Work on Time Series AI Modeling.
Prof. Chuzo Ninagawa is CEO of N Laboratory, Inc. and Professor of Smart Grid Power Control Engineering Joint Research Laboratory¸ Gifu University, Gifu, Japan. He has been Executive Chief Engineer of Mitsubishi Heavy Industries, Ltd., which is one of the largest hi-tech manufacturers in Japan. His research interests span various topics of smart grid, with special focus on virtual power plant (VPP) with a large-scale aggregation of fast automated demand responses. He has published over 110 academic papers and three advanced research books.
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