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Predictive Modelling for Energy Management and Power Systems Engineering

Editat de Ravinesh Deo, Pijush Samui, Sanjiban Sekhar Roy
en Limba Engleză Paperback – 29 sep 2020
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 management
  • Provides modeling theory in an easy-to-read format
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Specificații

ISBN-13: 9780128177723
ISBN-10: 0128177721
Pagini: 552
Ilustrații: 250 illustrations (125 in full color)
Dimensiuni: 191 x 235 mm
Greutate: 0.94 kg
Editura: ELSEVIER SCIENCE

Public țintă

Postgraduate researchers, early and mid-career scholars, expert academics, renewable energy practitioner, electrical and electronic engineers, climate scientists and future energy policy-makers

Cuprins

  1. A Multi-Objective Optimal VAR Dispatch Using FACTS Devices Considering Voltage Stability and Contingency Analysis NOUR EL YAKINE KOUBA
  2. PV panels lifespan increase by control Bechara NEHME
  3. Community-scale rural energy systems: General planning algorithms and management methods in developing countries A. López-González
  4. Proven ESS Applications for Power System Stability and Transition Issues Jean Ubertalli
  5. Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition Mohammad Rezaie-Balf, Sungwon Kim, Alireza Ghaemi and Ravinesh C. Deo
  6. Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia Ananta Neupane, Nawin Raj, Ravinesh Deo and Mumtaz Ali
  7. Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm Harshna Lata Gounder, Zaher Munder Yaseen and Ravinesh Deo
  8. Predictive Modeling of Oscillating Plasma Energy Release for Clean Combustion Engines Ming Zheng and Ramendra Prasad
  9. Nowcasting solar irradiance for effective solar power plants operation and smart grid management Viorel Badescu
  10. Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm Sagthitharan Karalasingham, Ravinesh Deo and Ramendra Prasad
  11. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products Ashkan Nabavi-Pelesaraei
  12. Support Vector Machine Models for Multi-Step Wind Speed Forecasting Shobna Prasad, Thong Nguyen-Huy and Ravinesh Deo
  13. MARS Model for Prediction of Short and Long-term Global Solar Radiation L.J.M. Deilki Tharaka Balalla, Thong Nguyen-Huy and Ravinesh Deo
  14. Wind Speed Forecasting in Nepal using Self Organizing Map-based Online Sequential Extreme Learning Machine (SOM-OSELM) Neelesh Sharma and Ravinesh Deo
  15. Potential growth in small-scale distributed generation systems in Brazilian capitals Julio Cezar M. Siluk
  16. The trend of Energy Consumption in Developing Nations for the last two decades: A case study from a statistical perspective Anshuman Dey Kirty