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.
Author(s): Chuzo Ninagawa
Publisher: Springer
Year: 2022
Language: English
Pages: 242
City: Singapore
Preface
Contents
About the Author
1 Introduction
1.1 Time Series
1.1.1 What is “Time Series” Dealt in This Book?
1.1.2 Time Series for Statistical Control
1.1.3 Dissemination of Time Series Data for Control
1.2 Time Series and Control Models
1.2.1 Control Modeling
1.2.2 Control Model Building Methods
1.3 Control Time Series and AI Methods
1.3.1 Control Model by Time Series Analysis
1.3.2 Control and AI Methods
References
2 Linear Time Series Modeling
2.1 Linear Regression Models
2.1.1 One-Dimensional Linear Regression Model
2.1.2 Multi-Dimensional Linear Regression Model
2.2 Fundamentals of AR Models
2.2.1 Overview of the AR Model
2.2.2 Yule-Walker Method (One Variable)
2.2.3 Yule-Walker Method (Multivariate)
2.3 Practical Example 1: Multiple Regression Model with Stable Interval
2.3.1 Air-Conditioning Stable Power Model
2.3.2 Selection of Explanatory Variables
2.3.3 Linear Multiple Regression Analysis
2.3.4 Model Evaluation and Validation
2.4 Practical Example 2: Step Response AR Model
2.4.1 Limited Control of Building Air-Conditioning Power
2.4.2 Fitting the AR Mathematical Model
2.4.3 Model Identification from Measured Data
2.4.4 AR Model Identification Results
References
3 Deep Learning AI Modeling
3.1 Fundamentals of Deep Learning
3.1.1 Fundamentals of Neural Networks
3.1.2 Principles of Deep Learning
3.1.3 Stacked Denoising Autoencoder Method
3.2 Time Series Data Deep Learning
3.2.1 Time Series Parallel Input Neural Network
3.2.2 Number of Layers for Time Series Deep Learning
3.2.3 Hyperparameters for Time Series Deep Learning
3.3 Practical Example 3: Step Response AR Neural Network
3.3.1 Step Response AR Neural Network
3.3.2 Training a Step Response Time Series Model
3.3.3 Evaluation of Step Response Time Series Models
3.4 Practical Example 4: Deep Learning in Practice—Sudden Event Prediction Model
3.4.1 Examples of Sudden Events
3.4.2 Sudden Event Prediction Neural Network Model
3.4.3 Training a Neural Network Model for Sudden Event Prediction
References
4 LSTM AI Modeling
4.1 Fundamentals of LSTM Neural Networks
4.1.1 What is LSTM Neural Network?
4.1.2 LSTM Forward Propagation Calculation
4.1.3 LSTM Back Propagation Calculation
4.2 Performance Evaluation Methods for LSTM Time Series Models
4.2.1 LSTM Model of Rare and Unexpected Events
4.2.2 Predictive Performance Evaluation Method
4.2.3 Results of Predictive Performance Evaluation
4.3 Practical Example 5: Electricity Wholesale Market LSTM Model
4.3.1 Prediction of Wholesale Electricity Prices
4.3.2 Electricity Wholesale Price LSTM Forecasting Model
4.3.3 Evaluation of Wholesale Electricity Price LSTM Forecasting Model
4.4 Practical Example 6: LSTM Model for Prediction of Disturbance Events
4.4.1 Example of a Time Series Unexpected Event
4.4.2 Facility Maintenance Operation as a Disturbance for RTP Adaptive Control
4.4.3 Disturbance Prediction LSTM Model for RTP Adaptive Control
4.4.4 Evaluation of Disturbance Predictive LSTM Model for RTP Adaptive Control
References
5 Optimal Control Using Time Series AI Models
5.1 Fundamentals of Optimal Search and Control
5.1.1 SA Optimal Search Method
5.1.2 Principle of Simulated Annealing (SA) Optimal Search Algorithm
5.1.3 Example of Evaluation Function for SA Optimal Search Control
5.2 State Explosion and Parallel Search
5.2.1 Large-Scale Control Target State Space
5.2.2 Parallel SA Search Algorithm
5.2.3 Trials of Large-Scale Parallel Search
5.3 Practical Example 7: Electricity Price Optimal Search Control
5.3.1 Real-Time Electricity Pricing
5.3.2 Optimal Control of Air-Conditioning Power Rates
5.3.3 Actual Equipment Tests of Optimal Search Control
5.4 Practical Example 8: Practical Cessation of Large-Scale Search
5.4.1 Practicality of Optimal Search Control
5.4.2 Censoring of Large-Scale Search
References
6 Reality of Time Series Learning Data Collection
6.1 Practical Example 9: Generating Training Data with Pseudo-step Response
6.1.1 Step Response Training Data
6.1.2 Break-Point Step Response Extraction Method
6.1.3 Example of Break-Point Method Training Data Collection
6.2 Practical Example 10: Artificial Augmentation of Training Data Collection
6.2.1 Reality of Training Data Collection
6.2.2 Artificial Augmentation of Training Data
6.2.3 Practice of Artificial Training Data Augmentation
6.3 Example 11: Generating Training Data with Emulators
6.3.1 Baseline and Reproducibility
6.3.2 Baseline Emulator Training
6.3.3 Baseline Estimation Model Evaluation
References
7 Practical Work on Time Series AI Modeling
7.1 IoT Time Series Data Collection Methods
7.1.1 IEEE1888 Standard for Time Series Data Collection
7.1.2 IEEE1888 Time Series Data Transmission Method
7.1.3 IEEE1888 Standard IoT Communication Software Implementation
7.2 Zone Selection for Time Series AI Training Data
7.2.1 Reality of Time Series Training Data Collection
7.2.2 Zone Selection of Time Series Training Data
7.2.3 Practical Methods with Time Series Data Selection
7.3 Self-developed Software for Time Series AI Modeling
7.3.1 Off-the-Shelf Training Tools
7.3.2 Self-developing Machine Learning Software
7.3.3 Visualization with Self-developed Machine Learning Software
References
8 Example Source Code of MLP Deep Learning Algorithm
8.1 Execution Environment
8.2 Example of MLP Code
9 Example Source Code of LSTM Neural Network Learning Algorithm
9.1 Execution Environment
9.2 Baseline Estimation LSTM Time Series Learning Code Example
