A new interval prediction method for displacement behavior of concrete dams based on gradient boosted quantile regression

2021 ◽  
Author(s):  
Qiubing Ren ◽  
Mingchao Li ◽  
Yang Shen
Keyword(s):  
Quantile Regression ◽  
Prediction Method ◽  
Concrete Dams ◽  
Interval Prediction ◽  
Displacement Behavior

scholarly journals Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

2019 ◽  
Vol 158 ◽  
pp. 6176-6182 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Jiantao Lu ◽  
Liangge Cheng
Keyword(s):  
Solar Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Interval Prediction ◽  
Long Short Term Memory

An optimized short-term wind power interval prediction method considering NWP accuracy

2014 ◽  
Vol 59 (11) ◽  
pp. 1167-1175 ◽  
Author(s):  
Yongqian Liu ◽  
Jie Yan ◽  
Shuang Han ◽  
Infield David ◽  
De Tian ◽  
...  
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Short Term ◽  
Interval Prediction

scholarly journals Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function

2019 ◽  
Vol 9 (5) ◽  
pp. 955 ◽  
Author(s):  
Gang Zhang ◽  
Zhixuan Li ◽  
Jinwang Hou ◽  
Kaoshe Zhang ◽  
Fuchao Liu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Interval ◽  
Prediction Method ◽  
Copula Function ◽  
Solution Set ◽  
Shaanxi Province ◽  
Model Parameters ◽  
Multi Objective ◽  
Interval Prediction ◽  
Conditional Copula

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

Confidence assessment and interval prediction for multi-input model via grey system theory

2018 ◽  
Vol 8 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Haoliang Wang ◽  
Xiwang Dong ◽  
Qingdong Li ◽  
Zhang Ren
Keyword(s):  
Confidence Interval ◽  
Correlation Coefficient ◽  
Calculation Method ◽  
Reference Sample ◽  
Prediction Method ◽  
Calculation Methods ◽  
Content Type ◽  
Interval Prediction ◽  
Confidence Value ◽  
Reference Samples

Purpose By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems. Design/methodology/approach In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true. Findings The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results. Originality/value Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.

scholarly journals Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

2021 ◽  
Vol 9 ◽  
Author(s):  
Mao Yang ◽  
Tian Peng ◽  
Xin Su ◽  
Miaomiao Ma
Keyword(s):  
Prediction Interval ◽  
Wavelet Packet ◽  
Mixture Distribution ◽  
Prediction Method ◽  
Support Vector ◽  
Average Width ◽  
Efficient Operation ◽  
Confidence Levels ◽  
Interval Prediction ◽  
Probability Prediction

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

scholarly journals Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

2020 ◽  
Vol 32 (6) ◽  
pp. 821-835
Author(s):  
Jing Luo
Keyword(s):  
Quantile Regression ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Time Data ◽  
Short Term ◽  
Internet Of Vehicles ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

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