A Short-Term Load Demand Forecasting based on the Method of LSTM

被引：10

作者：

Bodur, Idris ^{[1
]}

Celik, Emre ^{[2
]}

Ozturk, Nihat ^{[1
]}

机构：

[1] Gazi Univ, Technol Fac, Dept Elect & Elect Engn, Ankara, Turkey

[2] Duzce Univ, Engn Fac, Dept Elect & Elect Engn, Duzce, Turkey

来源：

10TH IEEE INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS (ICRERA 2021) | 2021年

关键词：

short term load forecasting; long-short-term memory; recurrent neural network;

D O I：

10.1109/ICRERA52334.2021.9598773

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Electricity energy is produced from another energy source like fossil source such as oil, coil, natural gas renewable energy sources such as hydraulic, wind, solar. Their storage in high amount is a problematic issue. Therefore, the balance between the power generation and demanded power must be satisfied at all times. This is an obligation especially for companies that generate, transmit and distribute electrical energy. In this paper, a short term load demand forecasting based on a long short term memory (LSTM) is addressed, which may help planning operators for Turkish electricity market. The results of advocated approach were compared by the ones based on recurrent neural network As a result, it is found that the proposed LSTM approach can predict especially daily and weekly demands with an accuracy more than 90%.

引用

页码：171 / 174

页数：4

共 15 条

[1] State-of-the-art in artificial neural network applications: A survey [J].

Abiodun, Oludare Isaac ;

Jantan, Aman ;

Omolara, Abiodun Esther ;

Dada, Kemi Victoria ;

Mohamed, Nachaat AbdElatif ;

Arshad, Humaira .

HELIYON, 2018, 4 (11)

[2]

[Anonymous], EXIST Transparency Platform

[3]

[Anonymous], 2015, UNDERSTANDING LSTM N

[4]

Dupond S., 2019, ANNU REV CONTROL, V14, P200

[5] On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach [J].

Farsi, Behnam ;

Amayri, Manar ;

Bouguila, Nizar ;

Eicker, Ursula .

IEEE ACCESS, 2021, 9 :31191-31212

[6] Forecasting of the electric energy demand trend and monthly fluctuation with neural networks [J].

Gonzalez-Romera, Eva ;

Jaramillo-Moran, Miguel Angel ;

Carmona-Fernandez, Diego .

COMPUTERS & INDUSTRIAL ENGINEERING, 2007, 52 (03) :336-343

[7]

Haliloglu E.Y., 2018, TURKISH J ENERGY POL, V3, P40

[8]

Hu L, 2020, CHIN CONT DECIS CONF, P5495, DOI 10.1109/CCDC49329.2020.9164387

[9] Short-term load forecasting based on the method of Genetic Programming [J].

Huo, Limin ;

Fan, Xinqiao ;

Xie, Yunfang ;

Yin, Jinliang .

2007 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS I-V, CONFERENCE PROCEEDINGS, 2007, :839-843

[10] Short-Term Residential Load Forecasting Based on Resident Behaviour Learning [J].

Kong, Weicong ;

Dong, Zhao Yang ;

Hill, David J. ;

Luo, Fengji ;

Xu, Yan .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (01) :1087-1088

← 1 2 →