This book fills a void in the field of power systems, as an invaluable compendium for researchers, practitioners, as well as graduate students. It covers everything from load and price forecasting, to post-storm service restoration times. It also introduces advanced methods of time series forecasting, as well as neural networks.
This book fills a void in the field of power systems, as an invaluable compendium for researchers, practitioners, as well as graduate students. It covers everything from load and price forecasting, to post-storm service restoration times. It also introduces advanced methods of time series forecasting, as well as neural networks.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Mohamed E. El-Hawary, PhD is Professor of Electrical and Computer Engineering at Dalhousie University. He is also the editor for the IEEE Press Power Engineering Series. His contributions to electrical engineering research, education, and industry cover more than forty years. His pioneering work in the economic operation of power systems and the application of computational intelligence techniques to power system operational problems has been cited in numerous textbooks and research monographs and more than 300 research articles. He has published multiple books with Wiley, including Principles of Electric Machines with Power Electronic Applications, 2nd Edition, and Introduction to Electrical Power Systems.
Inhaltsangabe
Preface and Acknowledgments vii Contributors ix 1. Introduction 1 Mohamed E. El-Hawary 2. Univariate Methods for Short-Term Load Forecasting 17 James W. Taylor and Patrick E. McSharry 3. Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems 41 Antonio Gómez-Expósito, Alicia Troncoso, Jesús M. Riquelme-Santos, Catalina Gómez-Quiles, José L.Martínez-Ramos, and José C. Riquelme 4. Electricity Prices as a Stochastic Process 89 Yunhe Hou, Chen-Ching Liu, and Harold Salazar 5. Short-Term Forecasting of Electricity Prices Using Mixed Models 153 Carolina García-Martos, Julio Rodríguez, and María Jesús Sánchez 6. Electricity Price Forecasting Using Neural Networks and Similar Days 215 Paras Mandal, Anurag K. Srivastava, Tomonobu Senjyu, and Michael Negnevitsky 7. Estimation of Post-Storm Restoration Times for Electric Power Distribution Systems 251 Rachel A. Davidson, Haibin Liu, and Tatiyana V. Apanasovich 8. A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models 285 Vitor Hugo Ferreira and Alexandre P. Alves da Silva Index 297
Preface and Acknowledgments vii Contributors ix 1. Introduction 1 Mohamed E. El-Hawary 2. Univariate Methods for Short-Term Load Forecasting 17 James W. Taylor and Patrick E. McSharry 3. Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems 41 Antonio Gómez-Expósito, Alicia Troncoso, Jesús M. Riquelme-Santos, Catalina Gómez-Quiles, José L.Martínez-Ramos, and José C. Riquelme 4. Electricity Prices as a Stochastic Process 89 Yunhe Hou, Chen-Ching Liu, and Harold Salazar 5. Short-Term Forecasting of Electricity Prices Using Mixed Models 153 Carolina García-Martos, Julio Rodríguez, and María Jesús Sánchez 6. Electricity Price Forecasting Using Neural Networks and Similar Days 215 Paras Mandal, Anurag K. Srivastava, Tomonobu Senjyu, and Michael Negnevitsky 7. Estimation of Post-Storm Restoration Times for Electric Power Distribution Systems 251 Rachel A. Davidson, Haibin Liu, and Tatiyana V. Apanasovich 8. A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models 285 Vitor Hugo Ferreira and Alexandre P. Alves da Silva Index 297
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
USt-IdNr: DE450055826
