scholarly journals New Optimal Weight Combination Model for Forecasting Precipitation

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Song-shan Yang ◽  
Xiao-hua Yang ◽  
Rong Jiang ◽  
Yi-che Zhang
Keyword(s):  
Genetic Algorithm ◽  
Forecast Accuracy ◽  
Prediction Method ◽  
Optimal Weight ◽  
New Model ◽  
Combination Model ◽  
Precipitation Forecasting ◽  
Optimal Weights ◽  
Ar Models ◽  
Beijing China

In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.

A Combination Model of Theil Coefficient and Induce Ordered Weighted Averaging (IOWA) Operator Based on Improved Bee Colony Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1547-1550
Author(s):  
Wei Xiang Gong ◽  
Guo Chu Chen ◽  
Zhao Hong Feng
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Weight Coefficient ◽  
Weighted Averaging ◽  
Optimal Weight ◽  
Combination Model ◽  
Ordered Weighted Averaging ◽  
Bee Colony ◽  
Iowa Operator

A combination model of Theil coefficient and Induce Ordered Weighted Averaging (IOWA) operator based on improved bee colony algorithm is proposed by introducing the Theil coefficient, IOWA arithmetic operators and bee colony algorithm. The model is built from correlation angle, and adopts IOWA arithmetic operators to make the weight coefficient of each model only related to prediction accuracy of the point at every time and has nothing to do with the prediction method. The optimal weight coefficient can be decided through bee colony algorithm, it can be showed that this model can reflect the wind power trend preferably, with which the prediction accuracy can be improved significant

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

A Calibrated Combination of Probabilistic Precipitation Forecasts to Achieve a Seamless Transition from Nowcasting to Very Short-Range Forecasting

2020 ◽  
Vol 35 (3) ◽  
pp. 773-791
Author(s):  
Peter Schaumann ◽  
Mathieu de Langlard ◽  
Reinhold Hess ◽  
Paul James ◽  
Volker Schmidt
Keyword(s):  
Real Time ◽  
Short Range ◽  
Lead Times ◽  
New Model ◽  
Combination Model ◽  
Logit Transformation ◽  
Interaction Terms ◽  
Probabilistic Forecasts ◽  
Seamless Transition ◽  
The Ideal

Abstract In this paper, a new model for the combination of two or more probabilistic forecasts is presented. The proposed combination model is based on a logit transformation of the underlying initial forecasts involving interaction terms. The combination aims at approximating the ideal calibration of the forecasts, which is shown to be calibrated, and to maximize the sharpness. The proposed combination model is applied to two precipitation forecasts, Ensemble-MOS and RadVOR, which were developed by Deutscher Wetterdienst. The proposed combination model shows significant improvements in various forecast scores for all considered lead times compared to both initial forecasts. In particular, the proposed combination model is calibrated, even if both initial forecasts are not calibrated. It is demonstrated that the method enables a seamless transition between both initial forecasts across several lead times to be created. Moreover, the method has been designed in such a way that it allows for fast updates in nearly real time.

scholarly journals Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

2016 ◽  
Vol 8 (8) ◽  
pp. 168781401666347 ◽  
Author(s):  
Milan Eric ◽  
Miladin Stefanovic ◽  
Aleksandar Djordjevic ◽  
Nikola Stefanovic ◽  
Milan Misic ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Production Process ◽  
Parameter Optimization ◽  
Process Parameter ◽  
Classification Method ◽  
Process Parameter Optimization ◽  
New Model ◽  
Model Based ◽  
Abc Classification
Sign in / Sign up

Export Citation Format

Share Document