Medium-term power load forecasting based on grey model in Ningxia Autonomous Region

This paper presents an improved grey model used in power load forecasting. In order to overcome the limitation of the traditional grey model GM(1,1), vector θ is introduced to modify the calculating formula for background sequence value in grey model and build a more adaptable model. Using artificial fish school algorithm can solve the value of vector θ . It reflects that the improved model has higher accuracy of load forecasting and has wider application by cases analysis.

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Power load forecasting based on Improved Grey Model of particle swarm optimization

10.1109/aemcse51986.2021.00057 ◽

2021 ◽

Author(s):

Xiang Li ◽

Liang Hu ◽

Liuhui Zhai

Keyword(s):

Particle Swarm Optimization ◽

Load Forecasting ◽

Particle Swarm ◽

Grey Model ◽

Swarm Optimization ◽

Power Load ◽

Power Load Forecasting

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Medium Term Power Load Forecasting for Java and Bali Power System Using Artificial Neural Network and SARIMAX

2018 5th International Conference on Data and Software Engineering (ICoDSE) ◽

10.1109/icodse.2018.8705837 ◽

2018 ◽

Author(s):

Andri Hardono Hutama ◽

Saiful Akbar ◽

Muhammad Zuhri Catur Candra

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power System ◽

Load Forecasting ◽

Medium Term ◽

Power Load ◽

Power Load Forecasting ◽

Artificial Neural

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GM (1,1) Model Application in Power Load Forecasting Based on Sliding Average Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.1300 ◽

2014 ◽

Vol 687-691 ◽

pp. 1300-1303

Author(s):

Li Zhi Song

Keyword(s):

Average Method ◽

Moving Average ◽

Load Forecasting ◽

Prediction Method ◽

Grey Model ◽

Grey Prediction ◽

Model Application ◽

Dispersion Degree ◽

Power Load ◽

Power Load Forecasting

The grey prediction method is simple in principle, the sample size was small and simple, suitable for load forecasting.But grey model has some limitations, the data dispersion degree is more bigger,the gray is also more bigger, it will reduce the accuracy of prediction.This paper adopts the moving average method to improve the raw data , so as to increase the data weights, while avoiding predicted value excessive volatility .Through a city of China's power load is instantiated to verify, and Then analyze the results, found that after the GM (1,1) model improved by moving average method can effectively improve the accuracy of load forecasting.

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