Hybrid advanced techniques for forecasting in energy sector / / edited by Wei-Chiang Hong.

• TeilnehmendeR: Hong, Wei-Chiang,
• Place / Publishing House: Basel, Switzerland : : MDPI - Multidisciplinary Digital Publishing Institute,, 2018.
• Year of Publication: 2018
• Language: English
• Physical Description: 1 online resource (250 pages)

Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression-chaotic quantum particle swarm optimization (SSVR-CQPSO), et cetera). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances. This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, id est, hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

Table of Contents

About the Special Issue Editor -- Preface to "Hybrid Advanced Techniques for Forecasting in Energy Sector" -- Guo-Feng Fan, Shan Qing, Hua Wang, Wei-Chiang Hong and Hong-Juan Li Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting Reprinted from: Energies 2013, 6, 1887-1901, doi: 10.3390/en6041887 -- Qun Niu, Zhuo Zhou, Hong-Yun Zhang and Jing Deng An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects Reprinted from: Energies 2012, 5, 3655-3673, doi: 10.3390/en5093655 -- Hongze Li, Sen Guo, Huiru Zhao, Chenbo Su and Bao Wang Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm, Reprinted from: Energies 2012, 5, 4430-4445, doi: 10.3390/en5114430 -- Ying-Yi Hong and Ching-Ping Wu Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Reprinted from: Energies 2012, 5, 4711-4725, doi: 10.3390/en5114711 -- Antonio Bracale, Pierluigi Caramia, Guido Carpinelli, Anna Rita Di Fazio and Gabriella Ferruzzi -- A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control Reprinted from: Energies 2013, 6, 733-747, doi: 10.3390/en6020733 -- Qian Zhang, Kin Keung Lai, Dongxiao Niu, Qiang Wang and Xuebin Zhang A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power Reprinted from: Energies 2012, 5, 3329-3346, doi: 10.3390/en5093329 -- Jaeyeong Yoo and Kyeon Hur Load Forecast Model Switching Scheme for Improved Robustness to Changes in Building Energy Consumption Patterns Reprinted from: Energies 2013, 6, 1329-1343, doi: 10.3390/en6031329 -- Miloš Božic, Miloš Stojanovic, Zoran Stajic and Dragan Tasic A New Two-Stage Approach to Short Term Electrical Load Forecasting Reprinted from: Energies 2013, 6, 2130-2148, doi: 10.3390/en6042130 -- Luis Hernandez, Carlos Baladrón, Javier M. Aguiar, Belén Carro, Antonio J. Sanchez-Esguevillas and Jaime Lloret Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks Reprinted from: Energies 2013, 6, 1385-1408, doi: 10.3390/en6031385 -- Georgios Anastasiades and Patrick McSharry Quantile Forecasting of Wind Power Using Variability Indices Reprinted from: Energies 2013, 6, 662-695, doi: 10.3390/en6020662 -- Félix Iglesias and Wolfgang Kastner Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns Reprinted from: Energies 2013, 6, 579-597, doi: 10.3390/en6020579 -- Cruz E. Borges, Yoseba K. Penya, Iván Fernández, Juan Prieto and Oscar Bretos Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings Reprinted from: Energies 2013, 6, 2110-2129, doi: 10.3390/en6042110 -- Claudio Monteiro, Tiago Santos, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado and M. Sonia Terreros-Olarte Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity Reprinted from: Energies 2013, 6, 2624-2643, doi: 10.3390/en6052624 -- Emanuele Ogliari, Francesco Grimaccia, Sonia Leva and Marco Mussetta Hybrid Predictive Models for Accurate Forecasting in PV Systems Reprinted from: Energies 2013, 6, 1918-1929, doi: 10.3390/en6041918.