Research on Short Term Load Forecasting of Power System

Wang, Li Zhong; Liu, Tie Nan; Zhao, Hui; Liu, Hong Bo

doi:10.4028/www.scientific.net/amr.433-440.1666

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…By solving this differential equation, the parameter values of the equation can be derived. Finally, the gray prediction model of the cumulative sequence is obtained, and the prediction is carried out [15][16]. The specific modeling process is as follows.…”

Section: Gm (11) Model Principle Introductionmentioning

confidence: 99%

Medium and Long-term Power Load Forecasting Based on Grey Markov Correction Model

2022

IJFS

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Medium and long-term load forecasting is a necessary prerequisite for distribution network planning, and is of great significance for the improvement of economic and social benefits of the power system. Aiming at the defects of low prediction accuracy and poor applicability of traditional grey forecasting model, a medium and long-term load forecasting method of power system based on Markov residual correction and improved grey theory is proposed. Based on the classical grey forecasting GM(1,1) model, this method first divides the state according to the relative residuals, calculates the Markov residual transition matrix, and then uses the equal-dimensional innovation method to construct the grey Markov correction forecast. Model, and finally use residual processing method to revise the prediction results. The simulation results based on an actual example of load forecasting of annual power consumption in Guangxi Province show that compared with the traditional grey forecasting model, the grey Markov residual correction model proposed in this paper has significant advantages in forecasting accuracy and applicability.

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Section: Gm (11) Model Principle Introductionmentioning

confidence: 99%

Medium and Long-term Power Load Forecasting Based on Grey Markov Correction Model

2022

IJFS

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“…With the continuous reforms taking place in the electricity market, accurate short‐term power load forecasting not only provides security for the stability and economic operation of the power grid but also serves as a basis for arranging power production and dispatch under the market environment. Short‐term power load forecasting generally refers to predicting the power load in the next 1 h to 1 week, which is an important part of power load forecasting . To date, researchers at home and abroad have conducted extensive explorations on short‐term load forecasting, which can be generally classified into classical prediction methods based on the time series prediction principle and intelligent prediction methods based on machine learning .…”

Section: Introductionmentioning

confidence: 99%

Short‐term load forecasting based on wavelet neural network with adaptive mutation bat optimization algorithm

Zhang

Liu

et al. 2018

IEEJ Transactions Elec Engng

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To improve the accuracy of short‐term load forecasting of power systems, according to the nonlinearity and uncertainty of short‐term load sequence, a short‐term power load forecasting method combined with wavelet neural network (WNN) and adaptive mutation bat optimization algorithm (AMBA), which is based on the variance of the population's fitness, is proposed in this paper. The model determines the mutation probability of the current optimal individual based on the variance of the population's fitness and the current optimal solution, performs Gaussian mutation on the global optimal individual, and carries out the second optimization on the bat individuals after mutation. AMBA is employed to optimize the network parameters of WNN, improving the prediction accuracy of WNN and speeding up its training. Then the AMBA‐WNN forecasting model is built. The AMBA‐WNN model is used to predict short‐term load of a certain city in China as a case study. The results show that the model can effectively improve the accuracy of short‐term load forecasting and has good practical significance. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Research on Short Term Load Forecasting of Power System

Cited by 2 publications

References 4 publications

Medium and Long-term Power Load Forecasting Based on Grey Markov Correction Model

Medium and Long-term Power Load Forecasting Based on Grey Markov Correction Model

Short‐term load forecasting based on wavelet neural network with adaptive mutation bat optimization algorithm

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