Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

• Päätekijä: Wei-Chiang Hong (Ed.) (auth)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: MDPI - Multidisciplinary Digital Publishing Institute 2018
• Aiheet: Computer science; hybrid models; energy forecasting; empirical mode decomposition; evolutionary algorithms; wavelet transform; quantum computing mechanism; support vector regression / support vector machines; chaotic mapping mechanism; extreme learning machine; fuzzy time series; kernel methods; spiking neural networks
• Linkit: DOAB: download the publication; DOAB: description of the publication

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...