Study of the medium-long load forecasting based on the identical dimension addition grey model

2011 ◽  
Author(s):  
Xinhui Du ◽  
Jingbo Bai ◽  
Qing Fan
Keyword(s):  
Load Forecasting ◽  
Grey Model ◽  
Identical Dimension

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.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

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.

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