Mid-long term load forecasting of the unstable growth sequence based on Markov chains screening combination forecasting models

2016 ◽  
Author(s):  
Dong-Liang Zhang ◽  
Jian Yan ◽  
Wei-Hua Wang ◽  
Xiu-Lan Yang
Keyword(s):  
Markov Chains ◽  
Load Forecasting ◽  
Growth Sequence ◽  
Forecasting Models ◽  
Combination Forecasting

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.

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.

Long-term load forecasting: models based on MARS, ANN and LR methods

2018 ◽  
Vol 27 (4) ◽  
pp. 1033-1049 ◽  
Author(s):  
Gamze Nalcaci ◽  
Ayse Özmen ◽  
Gerhard Wilhelm Weber
Keyword(s):  
Load Forecasting ◽  
Forecasting Models

scholarly journals Long-Term Load Forecasting Based on a Time-Variant Ratio Multiobjective Optimization Fuzzy Time Series Model

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaojuan Liu ◽  
Jian’an Fang
Keyword(s):  
Time Series ◽  
Multiobjective Optimization ◽  
Optimality Theory ◽  
Load Forecasting ◽  
Time Series Forecasting ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Forecasting Models

Load forecasting problem is a complex nonlinear problem linked with economic and weather factors. Long-term load forecasting provides useful information for maintenance scheduling, adequacy assessment, and limited energy resources for electrical power systems. Fuzzy time series forecasting models can be used for long-term load forecasting. However, the interval length has been chosen arbitrarily in the implementations of known fuzzy time series forecasting models, which has an important impact on the performance of these models. In this paper, a time-variant ratio multiobjective optimization fuzzy time series model (TV-RMOP) is proposed, and its performance is tested on the prediction of enrollment at the University of Alabama. Results clearly promote the forecasting accuracy as compared to the conventional models. A genetic algorithm is used to search for the length of intervals based on the training data while Pareto optimality theory provides the necessary conditions to identify an optimal one. The TV-RMOP model is applied for the long-term load forecasting in Shanghai of China.

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