Predictive Modelling for Energy Management and Power Systems Engineering

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy managementProvides modeling theory in an easy-to-read format INDICE: 1. A Multi-Objective Optimal VAR Dispatch Using FACTS Devices Considering Voltage Stability and Contingency Analysis2. PV panels lifespan increase by control 3. Community-scale rural energy systems: General planning algorithms and management methods in developing countries4. Proven ESS Applications for Power System Stability and Transition Issues5. Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition6. Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia7. Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm8. Predictive Modeling of Oscillating Plasma Energy Release for Clean Combustion Engines9. Nowcasting solar irradiance for effective solar power plants operation and smart grid management10. Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm11. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products12. Support Vector Machine Models for Multi-Step Wind Speed Forecasting13. MARS Model for Prediction of Short and Long-term Global Solar Radiation14. Wind Speed Forecasting in Nepal using Self Organizing Map-based Online Sequential Extreme Learning Machine (SOM-OSELM)15. Potential growth in small-scale distributed generation systems in Brazilian capitals16. The trend of Energy Consumption in Developing Nations for the last two decades: A case study from a statistical perspective

• ISBN: 978-0-12-817772-3
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 400
• Fecha Publicación: 01/10/2020
• Nº Volúmenes: 1
• Idioma: Inglés