TY  - BOOK
AU  - Wei-Chiang Hong (Ed.)
AB  - 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), etc.). 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, i.e., 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.
DO  - 10.3390/books978-3-03897-291-4
ID  - OAPEN ID: 29149
KW  - hybrid models
KW  - autoregressive moving average with exogenous variable (ARMAX)
KW  - energy forecasting
KW  - fuzzy group
KW  - quantile forecasting
KW  - evolutionary algorithms
KW  - quantum computing mechanism
KW  - cluster validity
KW  - support vector regression / support vector machines
KW  - artificial neural networks
KW  - principal component analysis
KW  - bayesian inference
L1  - https://www.mdpi.com/books/pdfview/book/841
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/49698
PB  - MDPI - Multidisciplinary Digital Publishing Institute
PY  - 2018
SN  - 9783038972914
SN  - 9783038972907
TI  - Hybrid Advanced Techniques for Forecasting in Energy Sectornull
ER  - 
