Comparison of forecasting methods for power system short-term load forecasting based on neural networks

Data pre-processing for short-term load forecasting in an autonomous power system using artificial neural networks

Proceedings of Third International Conference on Electronics, Circuits, and Systems ◽

10.1109/icecs.1996.584560 ◽

2002 ◽

Cited By ~ 8

Author(s):

S.J. Kiartzis ◽

C.E. Zoumas ◽

A.G. Bakirtzis ◽

V. Petridis

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Power System ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Autonomous Power System

Evaluation of Short-Term Load Forecasting Methods Using Dynamic Neural Networks

Communications in Computer and Information Science - Computational Intelligence and Information Technology ◽

10.1007/978-3-642-25734-6_50 ◽

2011 ◽

pp. 334-339

Author(s):

C. G. Mallamma ◽

Sateesh Chandra Reddy

Keyword(s):

Neural Networks ◽

Load Forecasting ◽

Short Term ◽

Dynamic Neural Networks ◽

Forecasting Methods ◽

Short Term Load Forecasting

Short-term load forecasting in an autonomous power system using artificial neural networks

IEEE Transactions on Power Systems ◽

10.1109/59.627863 ◽

1997 ◽

Vol 12 (4) ◽

pp. 1591-1596 ◽

Cited By ~ 30

Author(s):

S.J. Kiartzis ◽

C.E. Zoumas ◽

J.B. Theocharis ◽

A.G. Bakirtzis ◽

V. Petridis

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Power System ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Autonomous Power System

Application of Neural Networks for Short Term Load Forecasting in Power System of North Macedonia

2020 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) ◽

10.1109/icest49890.2020.9232674 ◽

2020 ◽

Author(s):

Goran Veljanovski ◽

Metodija Atanasovski ◽

Mitko Kostov ◽

Pande Popovski

Keyword(s):

Neural Networks ◽

Power System ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

A Combination of Pruning Algorithm and Parallel Networks Structure to Increase the Generalization of Neural Networks Used for Short-Term Load Forecasting of Iran Power System

2010 Asia-Pacific Power and Energy Engineering Conference ◽

10.1109/appeec.2010.5448648 ◽

2010 ◽

Author(s):

F. Amirarfaei ◽

M. B. Menhaj ◽

S. Barghinia

Keyword(s):

Neural Networks ◽

Power System ◽

Load Forecasting ◽

Short Term ◽

Pruning Algorithm ◽

Parallel Networks ◽

Short Term Load Forecasting ◽

Networks Structure

NEURO-FUZZY CONTROL IN LOAD FORECASTING OF POWER SECTOR

International Journal of Power System Operation and Energy Management ◽

10.47893/ijpsoem.2013.1092 ◽

2013 ◽

pp. 216-220

Author(s):

S.HEMA CHANDRA ◽

VENDOTI MOUNIKA ◽

GUNTURU VANDANA

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Fuzzy Control ◽

Power System ◽

Load Forecasting ◽

Power Sector ◽

Short Term ◽

Neuro Fuzzy ◽

Short Term Load Forecasting ◽

Energy Interchange

Load forecasting is of vital importance for any power system. It helps in taking many decisions regarding energy purchasing and generation, maintenance, etc. Further, load forecasting provides information which is able to be used for energy interchange with other utilities. Over the years, a number of methods have been proposed for load forecasting. This paper focuses on short term load forecasting by using a hybrid model of neural networks and fuzzy logic.

Short-term load forecasting using Theta method

E3S Web of Conferences ◽

10.1051/e3sconf/20198401004 ◽

2019 ◽

Vol 84 ◽

pp. 01004 ◽

Cited By ~ 1

Author(s):

Grzegorz Dudek

Keyword(s):

Empirical Studies ◽

Load Forecasting ◽

Seasonal Cycles ◽

Short Term ◽

Forecasting Accuracy ◽

Forecasting Methods ◽

Short Term Load Forecasting ◽

Data Definition ◽

System Data ◽

Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

Research on Short-Term Load Forecasting Method of Power System Based on Seq2Seq-Attention Model

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346583 ◽

2020 ◽

Author(s):

Wei Hu ◽

Yi Zhang ◽

Qiuting Guo ◽

Xu Huang ◽

Gang Li ◽

...

Keyword(s):

Power System ◽

Load Forecasting ◽

Short Term ◽

Attention Model ◽

Forecasting Method ◽

Short Term Load Forecasting

A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7980814 ◽

2017 ◽

Cited By ~ 6

Author(s):

Glen Mitchell ◽

Sanjay Bahadoorsingh ◽

Neil Ramsamooj ◽

Chandrabhan Sharma

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Vector Machines ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Load Types

A comparative study of short-term load forecasting methods in distribution network

Journal of Science, Quy Nhon University ◽

10.52111/qnjs.2021.15103 ◽

2021 ◽

Vol 15 (1) ◽

pp. 23-35

Author(s):

Tuan Ho Le ◽

◽

Quang Hung Le ◽

Thanh Hoang Phan

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Arima Model ◽

Distribution Network ◽

Load Forecasting ◽

Short Term ◽

Load Demand ◽

Forecasting Methods ◽

Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.