Deterministic Prediction Models Research Articles

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

An accurate method for predicting wind power is crucial in effectively mitigating wind energy fluctuations and ensuring a stable power supply. Nevertheless, the inadequacy of the stability of wind energy severely hampers the consistent functioning of the power grid and the reliable provision of electricity. To enhance the accuracy of wind power forecasting, this paper proposes an ensemble model named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional bidirectional long short-term memory (CNN-BiLSTM), which incorporates a data preprocessing technique, feature selection method, deep ensemble model, and adaptive control. Initially, CEEMDAN is utilized to decompose wind speed and power sequences and hence obtain decomposed subsequences for further analysis. Subsequently, the CNN is used to extract features from each subsequence, whereas each subsequence is processed by BiLSTM to obtain an ultra-short-term deterministic prediction model. Additionally, the adaptive kernel density estimation (AKDE) method is employed to estimate the probabilistic distribution of prediction error, enabling ultra-short-term probabilistic wind power prediction. Finally, based on real datasets, the reliability of the model in probabilistic prediction is verified through the evaluation metrics of multi-step prediction intervals (PIs).

Probabilistic assessment of anchor foundation for transmission tower using multivariate adaptive regression splines

ABSTRACT This paper pertains to the reliability analysis of transmission tower foundations under variable wind loads. The reliability analysis of the anchor foundation, frequently employed for transmission towers due to their huge uplift capacity at shallow depth, is presented using a systematic probabilistic approach. The paper presents the design of an anchor foundation for a typical transmission tower based on loads obtained by applying lateral wind velocity on the tower. The Multivariate Adaptive Regression Splines (MARS) surrogate model is first developed to construct a deterministic prediction model for the anchor uplift capacity. The developed model is further used in the reliability analysis of the anchor foundation considering variabilities in the soil properties and the uplift load, and the results of the probabilistic analysis are reported in the paper. The results indicate the importance of incorporating variability in design variables for the proper safety of the structure. Soil cohesion is found to be the most sensitive parameter in the uplift capacity of the tower foundation. The comparison of the deterministic factor of safety and failure probability of the foundation brings out the importance of the reliability-based design methodology.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis

The rapid development of the photovoltaic industry provides a new source of power for the continued operation of the over-consumed energy world. While providing new opportunities for global energy systems, it also poses challenges for power grids. Therefore, it is a priority to fully grasp the characteristics of photovoltaic changes and accurately forecast and analyze them. To enrich the existing research, a novel hybrid prediction system considering meteorological factors is constructed. First, a feature selection module is introduced to select features and assign weights to exogenous meteorological factors, which breaks through the limitations of single-data dimension prediction. Second, shallow and deep learning models are flexibly applied and multi-objective intelligent optimization strategies are introduced to construct deterministic combinatorial prediction models. The module can effectively increase the diversity of prediction models while fully weighing the accuracy and stability of prediction to meet the needs of different information users. Finally, an interval prediction model is constructed to further enrich the PV power prediction system from the perspective of uncertainty analysis. The empirical study is carried out with 5-minute interval data at three sites, and the results show that the hybrid system obtains superior out-of-sample forecasting performance with technical feasibility and general applicability.

The evaluation of EMI characteristics of 4G-IMT system on FAST around its RQZ

Mobile communication base stations have become ubiquitous with the popularization and development of mobile communication services. Although the Chinese government has designated a radio quiet zone (RQZ) specifically for Five-hundred-meter Aperture Spherical radio Telescope (FAST), the 4G International Mobile Telecommunications (4G-IMT) system is still heavily used to meet the communication needs around the RQZ. To assess the Electromagnetic Interference (EMI) caused by 4G-IMT base stations on the performance of FAST, this paper employs the International Telecommunication Union (ITU) deterministic radio wave propagation prediction model. The interference of 4G-IMT base stations on FAST is evaluated based on relevant parameters, such as the frequency used by both FAST and the base station, in conjunction with the FAST threshold protection requirements. The results indicate that 47.73 % of the data exceed the radio astronomy protection threshold of FAST. In addition, a field experiment was conducted to verify the accuracy of the radio wave propagation prediction method used. The root-mean-square error between the predicted value and the measured value was found to be 6.94 dB, supporting the accuracy of the predicted results. The findings of this study can help suppress interference from mobile communication services around the RQZ of FAST and optimize the electromagnetic environment around FAST.

Deterministic failure prediction of toughened glass when impacted by ice

The study presented in this paper deals with failure prediction of a thermally toughened glass panel caused by impact of ice. Ultimate failure was found from the study to be triggered by fast growth of cracks on the backside of the glass pane implying flexural failure. The deterministic predictive model developed in the study comprises closed form expressions which are without any calibrated coefficient. The input parameters are the size of the ice impactor, impact velocity, temperature of ice, glass pane thickness and the pre-existing stresses. Digital microscopy and micro-CT scanning techniques were employed to survey the dimensions of a purposedly preformed crack. As much of the uncertainties related to the critical crack has been resolved in the survey, no calibration of any modelling coefficient is required. Developing this new form of deterministic procedure was accomplished by capitalizing upon a model developed recently by the authors for predicting the (ice impact generated) transient stresses that are formed at the critical crack. The dynamic stress intensity can therefore be predicted conveniently by use of closed form expressions as opposed to (time consuming) numerical simulations. Accuracy of the model has been validated by impact testing involving accelerating ice impactors on the glass specimen by a gas gun. Twenty-one test cases involving two glass thicknesses, two sizes of ice spheres and seven preformed crack depths were conducted. Failure velocity was found to correlate strongly with crack depth, and was insensitive to changes in the crack length.

Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens

This study aims to develop deterministic and probabilistic prediction models for the multiple stress creep and recovery (MSCR) test. For this purpose, crumb rubber, polyphosphoric acid, and styrene–butadiene–styrene bitumen modifiers have been used with different dosages to modify high-temperature performance of PG 58–28 and PG 64–22 base bitumens. The MSCR test has been performed at different temperatures. Deterministic models are developed by the multi-gene genetic programming technique for each modifier individually, and the non-recoverable creep compliance (Jnr) and percent recovery (R) parameters are predicted. The accuracy of deterministic models is suitable and the performance of R models has been better than Jnr models. Furthermore, a comprehensive probabilistic model has been developed by using the logistic regression technique to predict different traffic levels. The accuracy of the probabilistic model is 0.85. The sensitivity analysis has been performed on this model and the effect of changes in the modifier dosage and temperature on the traffic levels have been investigated. Results show that using the probabilistic model, it is possible to find a range of modifier’s dosage in which the traffic level is desired.

Probabilistic prediction model of concrete carbonation depth considering the influence of multiple factors

AbstractIn order to realize the probability prediction of concrete carbonation depth, this paper proposed a probabilistic prediction model of carbonation depth based on 433 sets of existing experimental data. First, considering the influence of water‐binder ratio, fly ash content, stress level, and carbonation time, a deterministic prediction model of carbonation depth was established based on stepwise regression method. Then, based on the established deterministic prediction model, considering the influence of aleatory and epistemic uncertainties, a probabilistic prediction model of carbonation depth was established, and the posterior distributions of the parameters in the probabilistic prediction model were updated iteratively by using Bayesian theory and Markov chain Monte Carlo method, and the influence of standard deviation of prior distribution of the parameters in probabilistic prediction model on their posterior distributions was discussed. Finally, the prediction accuracy of the probabilistic prediction model was verified by comparing with another 127 sets of existing experimental data and the predicted values of the deterministic prediction model, and the effect of the standard deviations of prior distribution of the parameters on the prediction accuracy of the probabilistic prediction model was analyzed. The results showed that the prediction accuracy of the proposed probabilistic prediction model of carbonation depth after three updates was higher than that of the deterministic prediction model. When the standard deviation of prior distributions of the parameters in probabilistic prediction model was 100, the proposed probabilistic prediction model after three updates had the highest prediction accuracy. Under the tensile stress state of concrete, the prediction accuracy of the probabilistic prediction model was improved by 26.76% compared with the deterministic prediction model, and when the concrete under compressive stress state, the prediction accuracy of the probabilistic prediction model was 44.07% higher than that of the deterministic prediction model.

Reliability-based assessment of scour at pier in gravel beds

ABSTRACT Estimation of local scour depth at a bridge pier must be accurate and precise for the safety assessment of pier foundation. Many deterministic scour depth prediction models for bridge piers in sands and gravels have already been reported in the literature. However, the various types of uncertainties may be involved in the deterministic scour depth prediction models and thus affect the accuracy of the scour depth. To account for such uncertainties, a reliability analysis method is generally adopted. A sizable amount of laboratory and field data on scour has been procured in the present study to assess the performance of existing scour prediction models for gravel beds and develop a probabilistic scour prediction model using a First-Order Reliability Method (FORM) based on the object-oriented constraints optimization spreadsheet algorithm for the most appropriate selected deterministic scour prediction model. A new relationship of the silt factor has also been developed to estimate the scour depth in gravel bed as per Indian Practice. A safety factor based on the target reliability index has also been proposed in this study.

Spatially correlated Vs30 estimation in the Beijing area

Beijing is an international metropolis, that is also an earthquake-prone city. The aims of this study are detailed quantifying and qualifying soil layer properties for an accurate seismic safety evaluation in the Beijing area. The time average shear-wave velocity in the first 30 m of subsoil, Vs30, is an important site parameter used in site response analysis, site classification, and seismic loss estimation. Mapping of Vs30over a city-scaled region is commonly done through proxy-based methods by correlating Vs30with geological or topographic information. In this paper, a geostatistical-based random field model is presented and applied to mapping Vs30over extended areas. This random field model is then coupled with Monte Carlo simulations to obtain an averaged Vs30map and its associated uncertainties. Unlike the traditional deterministic prediction model, this framework accounts for spatial variations of Vs30values and uncertainties, which makes the prediction more reliable. A total of 388 shear wave velocity measurements in the Beijing area are used to calculate Vs30values, from which the statistical and spatial properties for the random field realizations are inferred. New spatially correlated probabilistic Vs30maps for the Beijing area are then represented, and the effect of the maximum number of previously generated elements to correlate to in estimating Vs30maps is tested.

Probabilistic prediction model of steel to concrete bond failure under high temperature by machine learning

The interface bond performance between concrete and steel bars under high temperature has a crucial influence on the fire resistance design of the reinforced concrete (RC) structure, but there is no unified model for the prediction of the bond strength yet. Previous experimental studies have conducted plenty of RC member pull-out tests under high temperature, which could be collected as a comprehensive database. As a data-driven method, machine learning (ML) can efficiently establish the regression relationship between input features and output directly through the data. However, current studies usually use classic ML algorithms for establishing a deterministic prediction model, where only one scalar prediction would be provided and its confidence level is uncertain, which is not instructive for the users. Thus, based on previous experimental data, this paper uses the Natural Gradient Boosting (NGBoost) algorithm to establish a unified probabilistic prediction model for the bond strength between steel bars and concrete under high temperature, considering several key factors, such as fiber fraction, concrete compressive strength, bond strength under room temperature et al. By training on the collected 267 experimental data, the prediction results of the ML based model show that these models attain higher accuracy than those of empirical formulas, and the NGBoost based probabilistic prediction model has better prediction performance than the general deterministic ML models. Finally, the Shapley value method is used to explain the calculation results of the model, and compared with the statistical results such as variance and mean of the calculation results of the five empirical formulas, ML has obvious advantages.

Deterministic combination prediction model of concrete arch dam displacement based on residual correction

The traditional deterministic model of arch dam displacement can separate the component effects of hydraulic, temperature, and aging. However, the prediction accuracy is not high, the residual sequence contains chaotic elements, and the dam prototype observation data have not been fully explored. Thus, a deterministic combination prediction model of arch dam displacement considering residual correction is proposed. According to the chaotic characteristics of the residual sequence of the deterministic model, multiscale permutation entropy is introduced to screen the intrinsic modal components with obvious noise, and the residual sequence is decomposed and reconstructed by the combined denoising method of adaptive empirical mode decomposition and wavelet packet. The residual sequence after reconstruction is predicted by identifying the high-and low-frequency characteristics of the signal and by using the long short-term memory network model, gated recurrent unit model, and auto-regressive integrated moving average model. Based on the superposition of residual prediction value and deterministic model prediction value, a deterministic combined prediction model of concrete arch dam displacement considering residual correction is established. The engineering case shows that compared with the fitting accuracy of the traditional model, the fitting accuracy of the combined model is greatly improved. This result can provide a reference for the displacement prediction of similar hydraulic structures.

Deterministic wave prediction model for irregular long-crested waves with Recurrent Neural Network

Real-time predicting of stochastic waves is crucial in marine engineering. In this paper, a deep learning wave prediction (Deep-WP) model based on the ‘probabilistic’ strategy is designed for the short-term prediction of stochastic waves. The Deep-WP model employs the long short-term memory (LSTM) unit to collect pertinent information from the wave elevation time series. Five irregular long-crested waves generated in the deepwater offshore basin at Shanghai Jiao Tong University are used to validate and optimize the Deep-WP model. When the prediction duration is 1.92s, 2.56s, and, 3.84s, respectively, the predicted results are almost identical with the ground truth. As the prediction duration is increased to 7.68s or 15.36s, the Deep-WP model’s error increases, but it still maintains a high level of accuracy during the first few seconds. The introduction of covariates will improve the Deep-WP model’s performance, with the absolute position and timestamp being particularly advantageous for wave prediction. Furthermore, the Deep-WP model is applicable to predict waves with different energy components. The proposed Deep-WP model shows a feasible ability to predict nonlinear stochastic waves in real-time.

A hybrid deep reinforcement learning ensemble optimization model for heat load energy-saving prediction

Accurate heat load forecasting is crucial to achieving feed-forward control and on-demand heat supply in the district heating system (DHS). However, accurate forecasting is difficult to achieve effective energy-saving because experimental data are often not optimal or the most energy-efficient. In this study, a hybrid heat load forecasting model with energy-saving consideration is proposed, which consists of similar sample selection approach, short-term forecasting model pool and deep reinforcement learning ensemble strategy. In similar sample selection module, a novel weighted Euclidean norm (EN) is used to select suitable similar samples set for the training samples set. In model pool module, six popular deterministic prediction models are constructed. For energy-saving consideration, the average of minimum and actual heat load is selected as the training target in the training samples set and similar samples set. In ensemble module, deep deterministic policy gradient (DDPG) is used to integrate the prediction results of the base predictors. In the case study, real data from a heat exchange station in Tianjin are used to perform the prediction of heat load 24 h, 72 h and 168 h, respectively. Experimental results show that: (a) The proposed hybrid model is able to capture the change of heat load. The average root mean square error for different prediction periods are 0.0825 MW, 0.1007 MW and 0.1152 MW, respectively; (b) The proposed hybrid model can reduce energy consumption. The energy-saving rate for different periods are 5.33%, 5.31% and 5.07%, respectively.

A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction

Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively.

Bayesian Spatio-Temporal grAph tRansformer network (B-STAR) for multi-aircraft trajectory prediction

Multi-Agent Trajectory Prediction is a critical and challenging component across different safety–critical engineering applications, e.g., autonomous driving and flight systems. Trajectory prediction tools are required for the next-generation air transportation system (NextGen). In practice, the prediction of aircraft trajectories needs to consider the impact of various sources, such as environmental conditions, pilot/controller behaviors, and potential conflicts with nearby aircraft. Huge uncertainties associated with these factors lead to the untrustworthiness of a deterministic trajectory prediction model. Moreover, the safety assurance in the near-terminal area is of specific interest due to the increased airspace complexity, where the instrument/visual flight rules are applied. In this work, we propose the Bayesian Spatio-Temporal grAph tRansformer (B-STAR) architecture to model the spatial and temporal relationship of multiple agents under uncertainties. It is shown that the proposed B-STAR achieves state-of-the-art performance on the ETH/UCY pedestrian dataset with UQ competence. Then, multi-aircraft near-terminal interactive trajectory prediction model is trained and validated with real-world flight recording data. The sensitivity study on the prediction/observation horizon and the graph neighboring distance threshold are performed. The code is available at https://github.com/ymlasu/para-atm-collection/tree/master/air-traffic-prediction/MultiAircraftTP.

Morphometric deterministic model for prediction of sediment yield index for selected watersheds in upper Narmada Basin

Soil erosion is common and has a wide range of spatiotemporal variability. It is crucial in determining sediment output, which is essential for proper watershed management. In this research, we propose morphometric deterministic models (MDM) for prediction of sediment yield index using morphometric parameters of 49 watersheds from Upper Narmada Basin of Madhya Pradesh state, India. For this purpose, Shuttle Radar Topography Mission generated Digital Elevation Model was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Principle Component Analysis has been applied for the most effective parameter estimation. The linear and nonlinear MDM were discovered to be suitable for the field of sediment research due to the high value of R2 (over 70%). The sediment yield forecasting is critical for taking the appropriate management measures in the watershed to reduce the sediment load in the reservoir and extend the life of the structure.

Probability prediction of short-term user-level load based on random forest and kernel density estimation

With the development of smart grids and the popularization of smart meters, grid companies have obtained a large amount of fine-grained user electricity consumption data, making it possible to forecast individual users’ electricity load. Traditional deterministic forecasting cannot measure the uncertainty of users’ future electricity consumption behavior. This paper aims to establish a short-term load probability forecasting model for individual users, which can output the probability prediction interval and the probability density curve. Firstly, historical data is applied to train a deterministic prediction model based on random forest. Then, when predicting the user load at a future time, the output of each tree in the forest is formed into a prediction set, and the kernel density estimation method is used to obtain the probability prediction result of the user load. The proposed RF-KDE method obtains promising results on a public dataset. It also shows the advantages of easy parameters adjustment and fast training speed. Besides, tests conducted on the public dataset confirm that the proposed method can be applied to users with different electricity consumption behaviors.

Importance sampling for probabilistic prognosis of sector-wide flight separation safety

This study develops a probabilistic simulation methodology for the en-route safety assessment of multiple aircraft flying within an airspace sector. This assessment is affected by multiple sources of uncertainty, such as external inputs such as wind gust, internal factors such as aircraft component failure, and model errors (e.g., in the aircraft dynamics model) and variability in observed trajectories. The concern is to evaluate whether any pair of aircraft fail to maintain a minimum required lateral separation, and the safety assessment problem involves the prognosis of separation violations between all possible aircraft pairs within the sector, over a duration of interest. Some of the uncertainty sources are modeled as random variables and others as random processes. We combine two deterministic trajectory prediction models – the kinematics-based Generalized National Airspace Trajectory-Prediction System (GNATS), and the dynamics-based Base of Aircraft Data (BADA) – to formulate a probabilistic aircraft dynamic model which also includes model errors as additional external inputs. A computationally efficient importance sampling-based scheme is developed for this safety assessment problem involving a large number of aircraft. This approach is demonstrated using historical flight data, for aircraft operating in the Houston air traffic sector.

A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems

In this paper, we propose a traffic signal control method in intelligent transportation and geoinformation systems, based on a deterministic predictive model. The method provides adaptive control based on traffic data, including data from connected and autonomous vehicles. The proposed method is compared with the state-of-the-art traffic signal control solutions: empirical control algorithms and reinforcement learning-based control methods. An advantage of the proposed method is shown and directions of further research are outlined.