Interval Prediction Method Research Articles

Gray relational analysis and SBOA-BP for predicting settlement intervals of high-speed railway subgrade

PurposeThe deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approachA high-speed railway subgrade settlement interval prediction method using the secretary bird optimization (SBOA) algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.FindingsUsing the SBOA algorithm to optimize the BP neural network, the optimal weights and thresholds are obtained, and the best parameter prediction model is combined. The data were collected from the sensors deployed through the subgrade settlement monitoring system, and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement, and the collected data are verified using the model.Originality/valueThe experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model, and the SBOA-BP model has a wider range of prediction intervals for a given confidence level, which can provide higher guiding value for practical engineering applications.

Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

<p>Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.</p>

Interval Forecast Method for Wind Power Based on GCN-GRU

Interval prediction is to predict the range of power prediction intervals, which can guide electricity production and usage better. To improve further improve the performance of the prediction interval, this paper aims to investigate he wind power interval prediction method based on the lower and upper bound evaluation (LUBE). Firstly, an improved loss function is proposed, which transforms multi-objective optimization problems into single-objective optimization with guidance of mathematical derivation. Afterward, the interval prediction results can be further improved through a combination of graph convolutional network (GCN) and gate recurrent unit (GRU). Then, the tree-structured Parzen estimator (TPE) optimization algorithm optimizes the GCN cell to find the optimal parameter configuration and maximize the performance of the model. Finally, in the experimental part, the proposed GCN-GRU with improved loss function is compared with some current mainstream neural networks. The results show that for any type of network, the improved loss function can obtain prediction intervals with better performance. Especially for the prediction interval based on GCN-GRU, the prediction interval normalized average width (PINAW) and prediction interval relative deviation (PIRD) can reach 6.75% and 46.99%, respectively, while ensuring the given prediction interval nominal confidence (PINC).

Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints

Short-term photovoltaic (PV) power interval prediction provides a basis for day-ahead power dispatching and generation planning. However, the current gridded numerical weather prediction (NWP) has poor matching in specific PV stations, and the lack of consideration of PV power mutation characteristics and historical correlation in interval prediction, which further limit the improvement of PV power prediction accuracy. In this regard, this paper proposes a novel short-term interval prediction strategy for PV power. Based on the second-order extended hidden Markov model (HMM), the key meteorological elements of the PV station with poor matching are reconstructed. In the interval prediction, the trend mutation and historical correlation characteristics of the PV sequence are fully considered, and a PV power interval prediction method that combines three factors such as trend change, time correlation and numerical mutation is proposed. The proposed method is applied to a PV station in Jilin, China. The results show that compared with other methods, the RMSE of the proposed method is reduced by 5.3 % on average, and the CWC is reduced by at least 2.1 %, which verifies the effectiveness of the proposed method.

An interval prediction approach for quantifying the thermal error uncertainty of ball screw system

The ball screw system is one of the most core components of CNC machine tool, and its thermal positioning error directly affects the machining accuracy of parts. Establishing a data-based thermal error model is currently a popular method to predict this thermal error. Uncertain disturbances are unavoidable in reality. However, most of the existing thermal error models are deterministic, which cannot consider the uncertainty and generally can only provide the predictive value. To quantify the uncertainty of thermal error from the ball screw system, this paper proposes an interval prediction method, in which gated recurrent unit (GRU) neural network is embedded into the model and prediction interval (PI) evaluation indexes are used to determine the model parameters. Both the temporal and spatial characteristics of thermal error in the ball screw system are taken into account by the proposed method. The experimental data has verified that the proposed method can effectively generate PIs for the thermal error of ball screw system.

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data.

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.

Research on methane Hazard interval prediction method based on hybrid “model-data”driven strategy

Safe mining of coal has an important impact on energy security, while effective control of methane hazard is the key to ensuring safe coal mining. Methane concentration is the main factor determining the hazard of methane in coal mines, and in order to limit the impact of methane on coal mine safety, this study proposes a methane concentration interval prediction method based on a hybrid “model-data” driven idea. Firstly, by analyzing the data and constructing a methane concentration prediction method based on model-driven, which reduces the influence of multicollinearity in the methane concentration series on the prediction effect, and then, in combination with the deep learning technique, a method based on the Wasserstein distance to improve the Informer model is proposed, and finally a hybrid-driven methane concentration interval prediction model is established by introducing the IOWGA operator and the statistical method. After an example analysis of a coal mine in Guizhou Province, China, the hybrid-driven model proposed in this study has better applicability and prediction accuracy in the methane concentration prediction task, which can effectively prevent the occurrence of coal mine accidents and is more in line with the needs of coal mine safety production.

Displacement Interval Prediction Method for Arch Dam with Cracks: Integrated STL, MF-DFA and Bootstrap

Displacement prediction models based on measured data have been widely applied in structural health monitoring. However, most models neglect the particularity of displacement monitoring for arch dams with cracks, nor do they thoroughly analyze the non-stationarity and uncertainty of displacement. To address this issue, the influencing factors of displacement were first considered, with crack opening displacement being incorporated into them, leading to the construction of the HSCT model that accounts for the effects of cracks. Feature selection was performed on the factors of the HSCT model utilizing the max-relevance and min-redundancy (mRMR) algorithm, resulting in the screened subset of displacement influence factors. Next, displacement was decomposed into trend, seasonal, and remainder components applying the seasonal-trend decomposition using loess (STL) algorithm. The multifractal characteristics of these displacement components were then analyzed by multifractal detrended fluctuation analysis (MF-DFA). Subsequently, displacement components were predicted employing the convolutional neural network-long short-term memory (CNN-LSTM) model. Finally, the impact of uncertainty factors was quantified using prediction intervals based on the bootstrap method. The results indicate that the proposed methods and models are effective, yielding satisfactory prediction accuracy and providing scientific basis and technical support for the health diagnosis of hydraulic structures.

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Research on interval prediction method of railway freight based on big data and TCN‐BiLSTM‐QR

AbstractWith the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm

Wind power prediction plays a pivotal role in increasing the power grid stability and mitigating market transaction risks. To enhance the prediction accuracy of wind power, this study presents a novel chaotic time series wind power point and interval prediction approach, focusing on data processing, as well as an improved coati optimization algorithm. First, the improved wavelet threshold denoising (IWTD) technique is constructed to eliminate the noise from the original wind power data. Then, the maximal information coefficient (MIC) is adopted to determine the optimal inputs of the prediction model. Subsequently, the improved coati optimization algorithm (ICOA) is developed to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM), deep belief network (DBN), and gated recurrent unit (GRU) models, along with the linear weight coefficients of the combined model, thereby enhancing the point prediction accuracy. Finally, kernel density estimation (KDE) is employed to obtain interval predictions for wind power with different confidence levels. Results show that the proposed prediction method effectively improves the prediction accuracy compared with other popular prediction models.

Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

An improved interval prediction method for recurrence period wind speed

Based on the improved interval operation theory, an improved expression of the return period wind speed interval prediction is constructed by using an approximate first-order Taylor series expansion. According to the measured wind speed data in Beijing, Jinan, Nanjing, Wuxi, Shanghai and Shenzhen, the improved method and the traditional method are respectively used to predict the interval of the return period wind speed. Furthermore, the interval results predicted by the improved method and the traditional method are compared and analyzed under the same confidence level. Results show that the improved method has good applicability for different parameter estimation methods under the condition of certain extreme value distribution model, and the interval prediction results of the return period wind speed are basically stable. Compared with the interval results predicted by the traditional method, the interval predicted by the improved method is more likely to be close to or contain the exact solution of the return period wind speed, which has higher prediction accuracy. In addition, the calculation process of the improved method is relatively simple and can realize the simplified calculation of interval prediction.

Deviation entropy-based dynamic multi-model ensemble interval prediction method for quantifying uncertainty of building cooling load

Interval prediction is a promising method that can reveal the uncertainty of building load and has been shown to effectively manage building energy systems. Previous studies focused on improving the performance of three types of generic single models (quantile regression, bootstrap, and lower upper bound estimation models). Multi-model ensemble methods have the advantages of reducing overfitting risk, improving stability. However, in the field of building cooling load interval prediction, there is a research gap that the performance of the multi-model ensemble method has not been conducted extensively. To compensate for this gap, firstly, referring to the information entropy in information theory, we proposed deviation entropy, which is a new concept that can be used to calculate the weight of a generic single model. Based on this, a static multi-model ensemble interval prediction method was developed. Then, we introduced a sliding time window to further upgrade the static method into a dynamic method. Finally, we used actual data to evaluate the performance of the proposed dynamic multi-model ensemble interval prediction method. The study results show that the multi-model ensemble method outperforms traditional generic single model, and the dynamic method can improve the prediction reliability by 8.5% with only 0.76% loss of prediction accuracy.

Probabilistic interval prediction method based on shape‐adaptive quantile regression

AbstractThis article introduces customized screening ensemble with shape‐adaptive quantile regression (CseAQR), a novel probabilistic interval forecasting method built upon the quantile regression model. CseAQR utilizes ensemble learning to perform adaptive quantile regression prediction, which can handle the heteroscedasticity feature in time series data by using a weighted adaptive allocation loss function to enhance the adaptability of the basic quantile regression model on the dataset. The model performance predictor is used to select the optimal ensemble learner combination, assign reasonable adaptive weights to it, and obtain a preliminary prediction interval through weighted aggregation. Combining ensemble learners not only improves the accuracy and robustness of prediction intervals but also ensures the commutativity required for conformal prediction. Finally, the conformal prediction method is applied to locally adjust the prediction interval, constructing a more consistently aligned prediction interval with the actual data on a narrower basis.

An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors

An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors

Physics informed ensemble learning used for interval prediction of fracture toughness of pipeline steels in hydrogen environments

Hydrogen embrittlement leads to a loss of plasticity and a decrease in fracture toughness in pipeline steels. Fracture toughness is a critical factor in the design and safety assessment of hydrogen-carrying pipelines. However, conducting large-scale fracture toughness testing on hydrogen-charged materials is time-consuming and expensive. Analytical approaches based on micromechanics often involve numerous undetermined (or assumed) parameters. Therefore, this study presents an approach that combines physical information and ensemble learning for predicting the fracture toughness range under hydrogen environments. Initially, detailed experimental data are gathered from a series of fracture tests conducted on pipeline steels under hydrogen environments, as published in the literature. Subsequently, Given the limited availability of experimental data, a feature space was constructed, taking into account the physical information as well as the distributions of yield strength and tensile strength. Furthermore, a predictive model based on Population-Based Training (PBT) optimization and Category Boosting (CatBoost) methodology was developed. Comparative experimentation substantiated its superior predictive accuracy in comparison to Random Forest, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). The MAE, MSE, R2, VAF, a20_index and MAPE, was 0.02139, 0.00096, 0.98741, 98.98%, 0.83 and 0.1552. Building upon these foundations, this study proposes an interval prediction method by combining Monte Carlo simulation with point prediction results to obtain fracture toughness interval prediction results with different confidence intervals (80%, 85%, 90% and 95%).

Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron

Low-carbon development is an inevitable choice under global climate change, and carbon markets have emerged accordingly. Carbon price forecasting is a crucial aspect of research on carbon-reduction strategies. Traditional forecasting methods are mostly focused on point-valued time series and contain limited information, which causes point forecasting to lose volatility information. Such limitations highlight the importance of proposing a precise interval-valued time series prediction system. To address this issue, we propose a carbon price interval forecasting framework based on a probability density recurrence network and interval multi-layer perceptron (MLPI) from the perspective of uncertainty in the presence of carbon price fluctuations. First, we convert the original data into a probability density sequence using kernel density estimation and then build the network. To extract valid information and exclude abnormal data in the carbon price sample data, we clean the data using network topology and link prediction theory; then, the MLPI model is used for interval forecasting. To verify the forecasting performance of this method, we conduct an empirical analysis using real interval-valued carbon futures price data from the EU carbon market and compare its forecasting precision with other models under the same conditions. The results show that the proposed carbon price interval prediction framework is superior to traditional methods in terms of forecasting precision.

A short-term photovoltaic power interval forecasting method based on fuzzy granular computing and CNN-BiGRU

Abstract This paper presents a short-term PV power interval prediction method combining fuzzy information granulation and CNN-BiGRU model. First, historical data of PV power generation is processed using fuzzy information granulation to determine the interval range. Subsequently, a CNN-BiGRU model is constructed, where the CNN module extracts local features of the interval range and the BiGRU module captures temporal patterns. The interval range is then fed into the CNN-BiGRU model for training, which enables accurate prediction of short-term PV power generation intervals. Finally, the predicted power generation interval ranges are given. The experimental results show that this hybrid interval prediction method is first applied to the prediction of short-term PV power scenarios, which fully utilizes the advantages of information granularity to express the data intervals and CNN-BiGRU to learn the complex time series patterns, and realizes the accurate and reliable prediction of short-term PV power intervals. Compared with direct numerical prediction, the method provides an expression of prediction uncertainty, which provides an important reference for the operation decision of PV power plants.