Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting
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...
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MDPI - Multidisciplinary Digital Publishing Institute,
2018.
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| 700 | 1 | 0 | |a CHIANG HONG,Wei |e author |
| 245 | 0 | 0 | |a Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2018. | ||
| 500 | |a http://oer.library.unej.ac.id//index.php?p=show_detail&id=606 | ||
| 500 | |a 624 | ||
| 520 | |a 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. | ||
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