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 Method Based on Improved Exponential Smoothing Grey Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jianwei Mi ◽  
Libin Fan ◽  
Xuechao Duan ◽  
Yuanying Qiu
Keyword(s):  
Prediction Model ◽  
Load Forecasting ◽  
Exponential Smoothing ◽  
Grey Model ◽  
Grey Prediction ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting ◽  
Exponential Smoothing Method

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

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.

Research on Mid-Long Term Load Forecasting Based on Gray Fourier Series Residual Correction Model

2014 ◽  
Vol 631-632 ◽  
pp. 345-349
Author(s):  
Dong Xiao Niu ◽  
Peng Wang ◽  
Qiong Wang ◽  
Bing Yi Liu ◽  
Wei Chang Zhang ◽  
...  
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Moving Average ◽  
Load Forecasting ◽  
Model Fitting ◽  
Original Data ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Power Load ◽  
Residual Correction

The purpose of power load forecasting is to provide the development status and level of the region's future load, providing the basis for the electric power production department and management department to make production and development plans. This paper puts forward the grey prediction model modified by Fourier series residual. First of all, the moving average method is used to preprocess the original data and weaken the burr of the original data. Secondly, the GM (1, 1) is used to estimate the load of the selected sample area. On this basis, use the Fourier series to revise the residual error of the grey forecasting model, making the model fitting of historical data as much as possible. The example analysis results show that the grey prediction model modified by Fourier series residual has the higher prediction accuracy compared with the general GM (1,1), proving the validity of the model.

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