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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
Postgraduate researchers, early and mid-career scholars, expert academics, renewable energy practitioners, electrical and electronic engineers, climate scientists and future energy policy-makers
- 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
3. Community-scale rural energy systems: General planning algorithms and management methods in developing countries
4. Proven ESS Applications for Power System Stability and Transition Issues
5. Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition
6. Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia
7. Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm
Harshna Lata Gounder
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
10. Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm
11. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products
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
Thong Nguyen-Huy, L.J.M. Deilki Tharaka Balalla and Ravinesh Deo
14. 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 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
- No. of pages:
- © Elsevier 2021
- 1st October 2020
- Paperback ISBN:
Dr Ravinesh Deo obtained BSc (with Gold Medal) from University of the South Pacific, MSc (Honours) from University of Canterbury and PhD from Adelaide University including Graduate Certificate in Tertiary Teaching from University of Southern Queensland. At The University of Queensland Dr Deo worked as Postdoctoral Fellow followed by Principal Scientist with Queensland Government. Currently, he is Senior Lecturer with significant doctoral supervision and project leadership at University of Southern Queensland. Dr Deo held Senior Visiting Researcher positons at United States Smithsonian Tropical Research Institute, McGill University, Chinese Academy of Science, University of Tokyo, including Kyoto and Kyushu University, Peking University, National University of Singapore and Universidad de Alcalá. Dr Deo is Associate Editor of ASCE Journal of Hydrologic Engineering, Editorial Board Member of Hydrology Research and Editor for Energies (SI). He won internationally prestigious fellowships and grants such as the Queensland Smithsonian Fellowship, Australia-China Young Scientist Award, Japan Society for Promotion of Science (JSPS) Fellowship, Chinese Academy of Science Fellowship and Australian Endeavour Fellowship. He teaches engineering mathematics, and leads artificial intelligence research whilst successfully supervised many doctoral and masters’ students. Dr Deo has published more than 150 peer reviewed papers that includes more than 100 Journal articles (high ranked), 2 Edited Books, Book Chapters and Conference papers in artificial intelligence, decision systems, energy, health informatics, and water and climate science.
School of Agricultural Computational and Environmental Sciences, Institute of Life Science and the Environment, University of Southern Queensland, Springfield, Australia
Dr Pijush Samui is Associate Professor, Department of Civil Engineering, NIT Patna, Bihar, India
Associate Professor, Department of Civil Engineering, NIT Patna, Bihar, India
Sanjiban Sekhar Roy is a Senior Associate Professor at the School of Computer Science and Engineering at VIT University. His primary courses focus on algorithm design and analysis, computer programming and problem solving, knowledge based systems, and agent based intelligence. He has published numerous journal articles on neural computing, neural networks, support vector machines, image processing and pattern recognition. He serves as a reviewer for the International Journal of Advanced Intelligence Paradigms and the International Journal of Artificial Intelligence and Soft Computing. He has received three publication awards for his research from VIT University.
Associate Professor (Senior), School of Computer Science and Engineering, VIT University, Vellore, India
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