Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

ISBN 10: 3038972924 / ISBN 13: 9783038972921

Verlag: Mdpi AG Okt 2018, 2018

Beschreibung

Neuware - 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.

Bestandsnummer des Verkäufers 9783038972921

Bibliografische Details

• Titel: Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting
• Verlag: Mdpi AG Okt 2018
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 3038972924
• ISBN-13: 9783038972921
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 244B x 170 H x 13 T Millimeter Breite: 244 mm, Höhe: 170 mm, Tiefe: 13 mm
• Gewicht: 408 Gramm

Über diesen Titel

Inhaltsangabe

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.