Application of improved grey prediction model for power load forecasting

2008 ◽  
Author(s):  
Wei Li ◽  
Zhu-hua Han
Keyword(s):  
Prediction Model ◽  
Load Forecasting ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Power Load ◽  
Power Load Forecasting

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.

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.

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.

Study on Power Load Forecasting Base on Grey Prediction Model by Slip Average Method

2011 ◽  
Vol 84-85 ◽  
pp. 752-756
Author(s):  
Zheng Yuan Jia ◽  
Zhi Wei Huang ◽  
Chun Mei Wang ◽  
Gang Zhang
Keyword(s):  
Prediction Model ◽  
Electric Power ◽  
Average Method ◽  
Original Data ◽  
Power Demand ◽  
Grey Prediction ◽  
Factors Affecting ◽  
Grey Prediction Model ◽  
Power Load ◽  
Set Up

The grey control theory is used to predict electric power demand in this paper. Original data is processed by the Generation Method. Many unimportant factors affecting electric power demand are removed,and useful information is extracted from original data. The differential fitting equation is set up,and grey prediction model modified by slip average method is presented with residual modification. The current year data is possessed with high weight,which avoids excessive fluctuation. Predicting results show that the model is effective to improve the predict precision.

scholarly journals A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1579
Author(s):  
Xinheng Wang ◽  
Xiaojin Gao ◽  
Zuoxun Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Prediction Model ◽  
Spectrum Analysis ◽  
Search Algorithm ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Power Sector ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electricity Load

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

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