scholarly journals Prediction Method of PV Output Power Based on Cloud Model

2017 ◽  
Vol 2017 (13) ◽  
pp. 1519-1523 ◽  
Author(s):  
Zhong Chen ◽  
Songyang Che ◽  
Yan Xu ◽  
Dapeng Yin
Keyword(s):  
Output Power ◽  
Cloud Model ◽  
Prediction Method

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

scholarly journals Novel Prediction Method for Highway Distresses in Permafrost Regions Based on Qualitative Reasoning of Multidimensional and Multirules Cloud Model

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Chi Zhang ◽  
Hong Zhang ◽  
Min Zhang ◽  
Quanli Gong
Keyword(s):  
Cloud Model ◽  
Prediction Method ◽  
Large Data ◽  
Qualitative Reasoning ◽  
Data Output ◽  
Infrastructure Project ◽  
Ice Content ◽  
Data Inference ◽  
Evaluation Parameters ◽  
Permafrost Regions

Highway in permafrost regions has numerous diseases during operation, due to instability and degradation of permafrost. To predict distress sections of a newly built highway in permafrost regions, we proposed a new method based on the multidimensional and multirules reasoning cloud model. Herein, the evaluation parameters affecting the highway distresses in permafrost regions, i.e., annual average ground temperature, ice content, and frozen-heave factor, were as the data input, whereas the distress degree was as the data output; all of the aforementioned were described by a cloud model. Based on the analysis of distress large data, inference rules and a cloud reasoning prediction model were established. Subsequently, distress degrees of the 10 equidistance highway sections were predicted on the Qinghai-Tibet highway by using the cloud model, and actual distress degree and predicted distress degree were compared by using the regression analysis algorithm. The results showed that the relevance between the actual distress degree and the predicted distress degree was 0.738. The study provides a feasible and effective method to predict the potential distress sections of the newly built highway and better plan infrastructure project on permafrost regions.

scholarly journals Modeling of Power Transition in Full Power Shift of Hydromechanical Transmission

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong Bao ◽  
Zaimin Zhong ◽  
Shujun Yang
Keyword(s):  
Output Power ◽  
Prediction Method ◽  
Transition Process ◽  
Transition Model ◽  
Power Transition ◽  
Object A ◽  
Target Value ◽  
Displacement Ratio ◽  
Power Shift ◽  
Full Power

This paper shall explore the dynamics of power transition in full power shift of hydromechanical transmission (HMT) and focus on the ideal target displacement ratio. An arithmetic two-range HMT is taken as the research object. A mathematical model of power transition in full power shift is established, including the hydraulic transmission unit model and the brake torque model during the double brakes overlapping. Aiming at the constant output power of HMT in the shift process, a prediction model of the displacement ratio target value is established, and the prediction method is proposed. By combining theoretical analysis and experimental research, it proves that the power transition model can describe the power transition process. And the prediction method of the displacement ratio target value proposed in this paper can complete the power transition when the double brakes overlap. In the power shift process, the output power can be transmitted normally in full power. The power transition model and the prediction method of the displacement ratio target value can provide theoretical and engineering references for the full power shift of HMT.

scholarly journals Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiachuang Wang ◽  
Mingjian Huang ◽  
Jiang Guo
Keyword(s):  
Compressive Strength ◽  
Rock Burst ◽  
Uniaxial Compressive Strength ◽  
Cloud Model ◽  
High Stress ◽  
Prediction Method ◽  
Deformation Energy ◽  
Prediction And Prevention ◽  
Calculation Results ◽  
Factor Interactions

Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.

scholarly journals Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

2021 ◽  
Vol 252 ◽  
pp. 01056
Author(s):  
Qiang Zhang ◽  
Gang Liu ◽  
Xiangzhong Wei
Keyword(s):  
Neural Network ◽  
Output Power ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Power Prediction ◽  
Power Station ◽  
Short Term ◽  
Factors Affecting ◽  
Photovoltaic Power

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

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