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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Descrizione libreria

I mainly carry imported books from South East Asia / South Asia for readers of all Age Groups.

Visita la pagina della libreria

Informazioni sull?azienda del venditore

PLETOS INC
6931 51st Avenue, WOODSIDE, Woodside, NY, 11377, U.S.A.

Condizioni di vendita e spedizione

Condizioni di vendita

We accept return for those books which are received damamged. Though we take appropriate care in packaing to avoid such situation.

Diritto di recesso

Condizioni di spedizione

Tariffe di spedizione nazionale per U.S.A.

Tariffe di spedizione nazionale per U.S.A.
Quantit� dell?ordine	Da 12 a 19 giorni lavorativi	Da 12 a 14 giorni lavorativi
Primo articolo	EUR 3.44	EUR 6.02

I tempi di consegna sono stabiliti dai venditori e variano in base al corriere e al paese. Gli ordini che devono attraversare una dogana possono subire ritardi e spetta agli acquirenti pagare eventuali tariffe o dazi associati. I venditori possono contattarti in merito ad addebiti aggiuntivi dovuti a eventuali maggiorazioni dei costi di spedizione dei tuoi articoli.