Application of the Improved Grey Model in Power Load Forecasting

2014 ◽  
Vol 575 ◽  
pp. 658-661 ◽  
Author(s):  
Yun Liang Wang ◽  
Qiao Yu Li
Keyword(s):  
Load Forecasting ◽  
Grey Model ◽  
Background Sequence ◽  
Fish School ◽  
Power Load ◽  
Power Load Forecasting ◽  
Improved Model

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.

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

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.

scholarly journals Short-term power load forecasting based on I-GWO-KELM algorithm

2021 ◽  
Vol 336 ◽  
pp. 05021
Author(s):  
Xiaoyu Chen ◽  
Xiangli Dong ◽  
Li Shi
Keyword(s):  
Meteorological Data ◽  
Load Forecasting ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Rbf Kernel ◽  
Improved Model ◽  
Kernel Parameters

In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.

An optimized grey model for annual power load forecasting

Energy ◽  
2016 ◽  
Vol 107 ◽  
pp. 272-286 ◽  
Author(s):  
Huiru Zhao ◽  
Sen Guo
Keyword(s):  
Load Forecasting ◽  
Grey Model ◽  
Power Load ◽  
Power Load Forecasting
Sign in / Sign up

Export Citation Format

Share Document