Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

Dublin Core

Title

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

Subject

General and Civil Engineering

Description

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.

Creator

Wei-Chiang Hong (Ed.)

Source

https://www.mdpi.com/books/pdfview/book/840

Publisher

MDPI - Multidisciplinary Digital Publishing Institute

Date

2018

Contributor

Baihaqi

Rights

Creative Commons

Format

PDF

Language

English

Type

Textbooks

Files

Citation

Wei-Chiang Hong (Ed.), “Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting,” Open Educational Resource (OER) - Unsyiah Library, accessed September 28, 2022, http://uilis.unsyiah.ac.id/oer/items/show/4517.

Document Viewer