Conversion of quarterly input data to demand prediction with high accuracy using adaptive neuro-fuzzy inference system: The case of Turkey.
Özet
Energy
is the most basic input in the production process for the realization of social
and economic development. The unstockable nature of electricity necessitates
designing a system that can always meet the demand. This is the most important
step of energy system planning. Medium-term prediction is important, especially
for Energy Systems management, in allocating production capacity, market
research and network maintenance planning. Adaptive Neuro Fuzzy Inference
System (ANFIS) is used for this study with the compilation of the obtained
three-month data for twenty years. ANFIS has obtained results with high
accuracy versus regression analysis even for crisis periods because of its
adaptive architecture covering the whole dynamic system structure. Gross
electricity demand could be predicted with % 96.74 accuracy by ANFIS as
compared previous studies
Sürdürülebilir Kalkınma Amaçları
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SKA 12: Sorumlu Üretim ve Tüketim
Atıf için künye
Ok, Y., & Atak, M. (2018). Conversion of quarterly input data to demand prediction with high accuracy using adaptive neuro-fuzzy inference system: The case of Turkey. JOURNAL OF TURKISH OPERATIONS MANAGEMENT, 50–56.
BibTeX
@article{ok2018-181692,
author = {Ok, Yeşim and Atak, Mehmet},
title = {Conversion of quarterly input data to demand prediction with high accuracy using adaptive neuro-fuzzy inference system: The case of Turkey.},
journal = {JOURNAL OF TURKISH OPERATIONS MANAGEMENT},
year = {2018},
pages = {50--56},
issn = {2630-6433},
url = {https://akademik.atauni.edu.tr/yayinlar/181692-conversion-of-quarterly-input-data-to-demand-prediction-with-high-accuracy-using}
}