Application of Machine Learning and Deep Learning Methods to Power System Problems

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.