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Description:
Intro -- Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case Studies -- Copyright -- Contents -- Contributors -- Foreword -- Chapter 1: Introduction -- 1.1. Who this book is for -- 1.2. Preview of the content -- 1.3. Power generation industry overview -- 1.4. Fuels as limited resources -- 1.5. Challenges of power generation -- References -- Chapter 2: Data science, statistics, and time series -- 2.1. Measurement, uncertainty, and record keeping -- 2.1.1. Uncertainty -- 2.1.2. Record keeping -- 2.2. Correlation and timescales -- 2.3. The idea of a model -- 2.4. First principles models -- 2.5. The straight line -- 2.6. Representation and significance -- 2.7. Outlier detection -- 2.8. Residuals and statistical distributions -- 2.9. Feature engineering -- 2.10. Principal component analysis -- 2.11. Practical advices -- References -- Chapter 3: Machine learning -- 3.1. Basic ideas of machine learning -- 3.2. Bias-variance-complexity trade-off -- 3.3. Model types -- 3.3.1. Deep neural network -- 3.3.2. Recurrent neural network or long short-term memory network -- 3.3.3. Support vector machines -- 3.3.4. Random forest or decision trees -- 3.3.5. Self-organizing maps -- 3.3.6. Bayesian network and ontology -- 3.4. Training and assessing a model -- 3.5. How good is my model? -- 3.6. Role of domain knowledge -- 3.7. Optimization using a model -- 3.8. Practical advice -- References -- Chapter 4: Introduction to machine learning in the power generation industry -- 4.1. Forecasting -- 4.2. Predictive maintenance -- 4.3. Integration into the grid -- 4.4. Modeling physical relationships -- 4.5. Optimization and advanced process control -- 4.6. Consumer aspects -- 4.7. Other applications -- References -- Chapter 5: Data management from the DCS to the historian and HMI -- 5.1. Introduction -- Key benefits.