Chapter 1. Power System Challenges and Issues
Chapter 2. Introduction and literature review of power system challenges and issues
Chapter 3. Machine learning and power system planning: opportunities, and challenges
Chapter 4. Introduction to Machine Learning Methods in Energy Engineering
Chapter 5. Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems
Chapter 6. Introduction and literature review of the application of machine learning/deep learning to load forecasting in power system
Chapter 7. A Survey of Recent particle swarm optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks
Chapter 8. Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods
Chapter 9. Voltage stability assessment in power grids using novel machine learning-based methods
Chapter 10. Evaluation and Classification of cascading failure occurrence potential due to line outage
Chapter 11. LSTM-Assisted Heating Energy Demand Management in Residential Buildings
Chapter 12. Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques
Chapter 13. Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning
Chapter 14. Prediction of Out-of-step Condition for Synchronous Generators Using Decision Tree Based on the Dynamic data by WAMS/PMU
Chapter 15. The adaptive neuro-fuzzy inference system model for short-term load, price and topology forecasting of distribution system
Chapter 16. Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances
Chapter 17. Machine Learning Approaches in a Real Power System and Power Markets.