Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting - Tapa blanda

 
9783038972921: Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting
Tapa blanda
ISBN 10: 3038972924 ISBN 13: 9783038972921
Editorial: MDPI AG, 2018

Sinopsis

The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models.

We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models.

We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
MDPI AG
Año de publicación
2018
Idioma
Inglés
ISBN 10 
3038972924
ISBN 13 
9783038972921
Encuadernación
Tapa blanda
Número de páginas
186
Editor
Hong Wei-Chiang
Contacto del fabricante
no disponible
Persona responsable
Buchpark GmbH
gpsr@buchpark.de

Krügerweg 1
Trebbin
Brandenburg
14959
Alemania