Mid-Long Term Load Forecasting Based on Fuzzy Soft Set and D-S Evidence Theory

2013 ◽  
Vol 732-733 ◽  
pp. 682-685
Author(s):  
Dong Xiao Niu ◽  
Lei Lei Fan ◽  
Qiao Ling Wu ◽  
Qing Guo Ma ◽  
Qin Liang Tan
Keyword(s):  
Load Forecasting ◽  
Evidence Theory ◽  
Soft Set ◽  
Forecasting Model ◽  
Fuzzy Soft Set ◽  
Combination Forecasting ◽  
Proposed Model ◽  
Combination Forecasting Model ◽  
Predicted Values

According to errors between the predicted values and the actual values, this paper establishes a fuzzy soft set in the form of membership function, then utilizes Dempster combination rule in evidence theory to synthesize the prediction results to obtain the weights of each single model, and thus builds a new hybrid combination forecasting model. The example shows that the proposed model can effectively improve the accuracy of mid-long term load forecasting, and is more accurate and credible than the combination forecasting model based on entropy or simply fuzzy soft set theory.

Substation Bus Reactive Load Forecasting in Large Iron and Steel Enterprise

2012 ◽  
Vol 433-440 ◽  
pp. 6168-6174
Author(s):  
Li Mu ◽  
Jia Chuan Shi ◽  
Xian Quan Li
Keyword(s):  
Reactive Power ◽  
Load Forecasting ◽  
National Standard ◽  
Forecasting Model ◽  
Impact Loads ◽  
Optimization Strategy ◽  
Reactive Power Optimization ◽  
Iron And Steel ◽  
Combination Forecasting ◽  
Combination Forecasting Model

Impact loads in large iron and steel enterprise bring the power system reactive power impact, which makes the fluctuation of the system voltage, power factor and other parameters are out of the limitation of the national standard. Substation bus reactive load forecasting in large iron and steel enterprise can be introduced to determine reactive power optimization strategy and the switching of capacitors. In this paper, a combination forecasting model of quadratic self-adaptive exponential smoothing (QSES) model and converse exponential (CE) model has been proposed for substation bus reactive load forecasting. The numerical results in Jinan iron and steel Group show the application of this model is encouraging. Introduction

scholarly journals Application of Combination Forecast Model in the Medium and Long Term Power Load Forecast

2012 ◽  
Vol 490-495 ◽  
pp. 1362-1366 ◽  
Author(s):  
Ke Zhao ◽  
Lin Gan ◽  
Zhong Wang ◽  
Yan Xiong
Keyword(s):  
Load Forecasting ◽  
Forecast Model ◽  
Combination Forecast ◽  
Combination Forecasting ◽  
Power Load ◽  
Forecasting Method ◽  
Model Predictions ◽  
High Prediction ◽  
Predicted Values

For seasonal and long-term power load forecasting problem, this paper presents an optimal combination forecasting method, which can optimize the combination of multiple predictive models. Optimize the combination of the two model predictions with two models as an example, which are the gray GM(1,1) model and linear regression model, and finally compare the predicted values of combination with the real values. The results show that: the combination forecasting method has a high prediction accuracy, and the error is very small.

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