Fallah, N.; Ganjkhani, M. Computational Intelligence Load Forecasting: A Methodological Overview. Preprints2018, 2018120235. https://doi.org/10.20944/preprints201812.0235.v1
APA Style
Fallah, N., & Ganjkhani, M. (2018). Computational Intelligence Load Forecasting: A Methodological Overview. Preprints. https://doi.org/10.20944/preprints201812.0235.v1
Chicago/Turabian Style
Fallah, N. and Mehdi Ganjkhani. 2018 "Computational Intelligence Load Forecasting: A Methodological Overview" Preprints. https://doi.org/10.20944/preprints201812.0235.v1
Abstract
Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
