Predictive Modelling for Energy Management and Power Systems Engineering

Erscheinungsjahr 2020
ISBN: 978-0-12-817772-3
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 552 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1260 g

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.

Zielgruppe

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

Autoren/Hrsg.

Deo, Ravinesh

Samui, Pijush

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik

Inhaltsverzeichnis

- A Multi-Objective Optimal VAR Dispatch Using FACTS Devices Considering Voltage Stability and Contingency Analysis NOUR EL YAKINE KOUBA

- PV panels lifespan increase by control Bechara NEHME

- Community-scale rural energy systems: General planning algorithms and management methods in developing countries A. López-González

- Proven ESS Applications for Power System Stability and Transition Issues Jean Ubertalli

- Forecasting solar radiation with evolutionary polynomial regression, wavelet transform & ensemble empirical mode decomposition Mohammad Rezaie-Balf, Sungwon Kim, Alireza Ghaemi and Ravinesh C. Deo

- Development and Comparison of Data-driven Models for Wind Speed Forecasting in Australia Ananta Neupane, Nawin Raj, Ravinesh Deo and Mumtaz Ali

- Modelling Photosynthetic Active Radiation with a Hybrid Multilayer Perceptron-Firefly Optimizer Algorithm Harshna Lata Gounder, Zaher Munder Yaseen and Ravinesh Deo

- Predictive Modeling of Oscillating Plasma Energy Release for Clean Combustion Engines Ming Zheng and Ramendra Prasad

- Nowcasting solar irradiance for effective solar power plants operation and smart grid management Viorel Badescu

- Short-term energy demand modelling with hybrid emotional neural networks integrated with genetic algorithm Sagthitharan Karalasingham, Ravinesh Deo and Ramendra Prasad

- Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in energy modeling of agricultural products Ashkan Nabavi-Pelesaraei

- Support Vector Machine Models for Multi-Step Wind Speed Forecasting Shobna Prasad, Thong Nguyen-Huy and Ravinesh Deo

- MARS Model for Prediction of Short and Long-term Global Solar Radiation L.J.M. Deilki Tharaka Balalla, Thong Nguyen-Huy and Ravinesh Deo

- Wind Speed Forecasting in Nepal using Self Organizing Map-based Online Sequential Extreme Learning Machine (SOM-OSELM) Neelesh Sharma and Ravinesh Deo

- Potential growth in small-scale distributed generation systems in Brazilian capitals Julio Cezar M. Siluk

- The trend of Energy Consumption in Developing Nations for the last two decades: A case study from a statistical perspective Anshuman Dey Kirty

Über Autor(innen)

Deo, Ravinesh
Professor Ravinesh Deo is an Associate Professor at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence. He also serves as Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks and long short-term memory networks. He was awarded internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, won Publication Excellence Awards, Head of Department Research Award, Dean's Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence and he was the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at United States Tropical Research Institute, Chinese Academy of Science, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange programs in Singapore, Japan, Europe, China, USA and Canada and secured international standing by researching innovative problems with global researchers. He has published Books with Springer Nature, Elsevier and IGI and over 190 publications of which over 140 are Q1 including refereed conferences, Edited Books and book chapters. Professor Deo's papers have been cited over 4,000 times with Google Scholar H-Index of 36 and a Field Weighted Citation Index exceeding 3.5.

Samui, Pijush
Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.