Search

The accidental leakage of gaseous hydrogen from hydrogen refueling station facilities has the potential to result in a large fire and explosion catastrophe. The ability to forecast the spatiotemporal evolution of hydrogen plumes in real-time is crucial for monitoring the distribution of plume concentrations and mitigating the risk of fire and explosion. The utilization of deep learning has been extensively employed in a diverse range of spatiotemporal forecasting tasks. However, these approaches encounter limitations in accurately quantifying uncertainty when predicting spatiotemporal concentrations and plume boundaries. This research paper introduces a novel model called DPSTNN_H 2 Evolution, which is a deep probabilistic spatial-temporal neural network designed for forecasting the spatiotemporal evolution of hydrogen plumes. The benchmark dataset is constructed by the implementation of numerical simulations pertaining to the inadvertent leak of hydrogen within a hydrogen refueling station. The findings of the study indicate that incorporating quantified uncertainty information might enhance the precision of plume boundaries and the resilience of plume concentrations when predicting the spatiotemporal development of hydrogen. By employing Monte Carlo sampling with a sample size of m = 100 and a dropout rate of p = 0.1 , the model demonstrates the ability to provide real-time inference for 10 instances of plumes, while maintaining a high level of accuracy with R2 = 0.9829. In comparison to the current state-of-the-art model, the proposed model demonstrates superior accuracy and robustness in forecasting the spatiotemporal evolution of hydrogen plumes. In general, our proposed model has the potential to serve as a dependable option for the development of a digital twin for emergency management of hydrogen refueling stations. • Probabilistic spatial-temporal neural network model for hydrogen plume evolutions is proposed. • Normalized uncertainty contours and uncertainty intervals to improve accuracy and robustness. • Numerical experiment of hydrogen leakage and dispersion in refueling station are conducted. • Optimal hyper-parameters of the proposed model are determined • Comparison between the proposed model with point-estimation based model is conducted [ABSTRACT FROM AUTHOR]

Accurate forecasting of regional solar photovoltaic power (SPVP) generation is essential for efficient energy management and planning. Existing approaches have shown the effectiveness of decomposing the time series to model the stochastic variability in SPVP data. However, these approaches have limitations in extracting and exploiting both spatial and temporal information from complex and high-dimensional data from multiple sources with intricate relationships, which can impact the accuracy of predictions. In this paper, we propose a novel approach called multilevel data fusion and neural basis expansion analysis (MF-NBEA) for forecasting aggregated regional-level SPVP generation. MF-NBEA integrates exogenous data at multiple levels, uses supervised and unsupervised encoders to provide compact data representation, and enhances model learning from complex data by incorporating spatial information. It also includes a sequence analyser module based on a neural network decomposition mechanism to learn the variability in data and incorporates a residuals learner module to improve overall predictions. We evaluate MF-NBEA using two real-world datasets and find that it outperforms state-of-the-art deep learning methods in terms of forecast accuracy. Furthermore, MF-NBEA facilitates information fusion and knowledge extraction to provide interpretable predictions regarding trend, seasonality, and residual components. The insights gained from our approach inform decision-making for energy management and planning, and can lead to more efficient and sustainable resource utilisation. • MF-NBEA for multivariate solar power time series forecasting. • A 3D autoencoder encodes spatial and temporal information from heterogeneous sources. • Interpretable forecasting in terms of trend, seasonality, and residuals. [ABSTRACT FROM AUTHOR]

This paper aims to incorporate the throttle handle level prediction in high speed train(HST) operation prediction problem to enable the prediction of HST drivers' activities, in which the key instructions available to HST driver are difficult to determine. Specifically, we consider an end-edge-cloud orchestration system to capture the real-time responses for driver state changes. By adding edge computing nodes, the real-time performance of data collection, transmission, and processing is improved. Our ultimate goal is to guide and regulate train drivers' activities in the same way, regardless of uncertain factors affecting HST dynamic or kinematic performance. We formulate the problem as a physical-based and data-driven deep learning-aided prediction model and solve it using a novel long short-term memory (LSTM) deep neural network which combines: (i) an off-line approximate training model to learn the time series data in the cloud layer, and (ii) an online prediction process to determine driving strategies in the real-time windows, more in general expressed as driving skill level constraints. To evaluate the performance of our approach, some case studies using the real-world railway infrastructure and HST data have been conducted. The results show that the proposed models produce higher prediction accuracy for both speed and throttle handle level prediction tasks. Compared to the conventional HST operation prediction problem, which considers speed sequences only without throttle handle level consideration, this study finds that jointly modeling speed and throttle handle level actually improves the next operation prediction performance itself, potentially because throttle handle level observations capture the information on HST control dynamics, which may affect operators' driving choices. • Structure. Edge computing and prediction scheme are innovatively combined with HST data collection to meet the delay-sensitive and sequential-aware operation requirements, and a novel HST operation structure for end-edge cloud orchestration system is built. • Modeling. A combination of the physical-based and the data-driven deep learning model is formulated for HST operation prediction under uncertainty in throttle handle level and speed, which is a new model in the literature. As discussed, the existing research has only considered speed dynamics in real-time DAS limitedly. • Practice. From the field collected data in Beijingxi–Zhengzhoudong HSR (as shown in Figure 5), we test the effectiveness of our proposed model with seven cases. Since the real-time driving advice is calculated in a few milliseconds, our two-phase solution method can be practically relevant when designing DAS or ATO systems. More importantly, most time-consuming calculations are executed off-line in the cloud layer and the amount of on-line calculations in edge layer is limited to looking up a table. [ABSTRACT FROM AUTHOR]

Wind, as a fluid, has continuity in both space and time. Coupling spatial and temporal information to build prediction models can improve wind speed prediction accuracy. This paper proposes a method that predicts wind speed at multiple locations using both spatial and temporal data. Three deep learning models are introduced: Convolutional Residual Spatial-Temporal Long Short-Term Memory neural network (CoReSTL), Convolutional Spatial-Temporal-3D neural network (CoST-3), and Convolutional Spatial-Temporal Long Short-Term Memory neural network (CoST-L). These models combine Convolutional Long Short-Term Memory (ConvLSTM), Residual Network (ResNet), and 1 × 1 3D convolution to extract spatial and temporal correlations between multi-site wind speeds. The spatio-temporal prediction of wind fields under two terrains was carried out to screen out neural network models with higher accuracy. The results show that CoReSTL, CoST-3, and CoST-L all achieved better prediction results. However, the performance of the CoReSTL model was better than that of CoST-3 and CoST-L, with stronger applicability in complex real terrain. • A multi-sites wind speed prediction methodology that leverages both spatial and temporal correlations. • ConvLSTM, ResNet, and 3D convolution are combined as the prediction model. • The proposed prediction method works well with ideal 3D hills and real complex mountainous terrain. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal graph neural network capable of effective representation learning of the spatiotemporal interrelationships and interdependencies of in-situ observation data from multiple locations for multivariate multi-step ahead time-series forecasting. The propose model is largely composed of graph learning, spatial encoder, and temporal decoder, and ablation studies on variants of the three modules and comparative experiments with state-of-the-art deep neural networks for sequence modeling were also performed extensively. The proposed model showed improved predictability than conventional numerical model-based approaches or state-of-the-art models by applying consecutive multi-step ahead time-series prediction of sea surface temperature at multiple locations along the coast. For more rigorous performance evaluation, not only the overall performance of the test data, but also the performance of extreme cases included in the test data based on historical records were separately assessed. The prediction rationales were also presented through quantified relative contributions between neighbor locations using the trained adjacency matrix obtained through graph learning. The results showed that it is well consistent with the ocean physics and geographical domain knowledge, demonstrating the feasibility and reliability of the proposed method. Therefore, the proposed method shows sufficient potential to be used as a scientific tool for decision-making in extreme events such as marine heat waves or for operational ocean forecasting. [ABSTRACT FROM AUTHOR]

Process mining focuses on the analysis of event log data to build various process analytical capabilities. Predictive process analytics has emerged as one of such key capabilities and it uses machine learning techniques to construct process prediction models. In recent years, deep neural networks have gained increasing interest in process prediction since they can handle multi-dimensional sequential inputs with minimal information loss. However, they are considered black-box models and existing studies in explaining deep neural network-based process predictions rely on only event-level features for explanation. In this paper, we propose a new approach for generating explanations for process outcome predictions at multiple levels. The approach is underpinned by three different prediction models: a transparent model for generating global explanations based on case-level features, an attention-based deep neural network for generating local explanations based on event-level features, and a novel eXplainable Dual-learning Deep network (XD 2 -net) for generating local explanations based on case-level features. Using three publicly available datasets, we have tested the applicability of the approach and further examined the multi-level explanations generated by the approach through an elaborate case study. Unlike others, the design of our approach promotes the idea of leveraging the complementary capabilities of different models and utilizing their strengths, rather than focusing on model performance competition. This will contribute towards generating more comprehensive explanations that meet the needs of different end users and purposes in the future. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves "throwing" a large amount of data at "black-box" software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice. [Display omitted] • Commonly held myths about artificial neural network (ANN) modelling are articulated and busted. • Good modelling practices applied to other types of predictive models are also applicable to ANNs. • State-of-the-art approaches to developing ANN models for prediction and forecasting are presented. • Size matters – ANNs with more inputs and hidden nodes are more likely to give non-sensical results. • Using an independent validation set is insufficient to ensure ANNs can be used with confidence in practice. [ABSTRACT FROM AUTHOR]

Well production forecasting has a very important guiding significance for oilfield production and management. The traditional BP neural network is difficult to deal with the data with time continuity, and the recurrent neural network (RNN) has some shortcomings such as vanishing gradients and exploding gradients. In order to avoid these problems and further improve the accuracy of neural network models in well production prediction. In this study, a prediction model combining temporal convolutional network (TCN) and attention mechanism is proposed. TCN is able to obtain more sensory fields to solve longer time series with fewer network layers by causally expanding convolution. And the residual connectivity in the model structure enables the model to transfer information across layers to solve the problem of network degradation caused by overly deep networks. The attention mechanism can reinforce the influence of important features on the prediction results to improve the prediction accuracy of the model. To evaluate the effectiveness of the model, the TCN-attention model was used to forecast the oil production of four producing wells, and the prediction results were compared with those of LSTM, TCN, RNN, and ARIMA. The results showed that TCN-Attention outperformed the other models in the prediction of the four wells in all evaluation metrics. The method proposed in this paper has excellent data fitting ability, can provide accurate prediction results, and have a certain application value in oilfield production. • The highlights of the paper are: • The model achieves better performance in forecasting than conventional models. • Temporal Convolutional Network can better extract time-series features. • Attention mechanism can effectively improve the model prediction accuracy. [ABSTRACT FROM AUTHOR]

Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models. • An advanced deep neuroevolution model is proposed for probabilistic load forecasting. • The proposed algorithm automatically optimizes parameters of the deep neural network. • Mean scaled interval score is used as fitness function for the evolutionary algorithm. • The proposed model has been tested on a load dataset, being easily applied to others. [ABSTRACT FROM AUTHOR]

[Display omitted] • In the case of a small number of known deposits and mineral occurrences, the prospecting prediction model based on the Siamese network can extract effective features without relying on prior knowledge to realize supervised prospecting prediction. • On the basis of sliding window data enhancement, this paper generates paired sample data sets by randomly combining deposit samples and non-deposit samples, which can further enhance the data and improve the generalization ability and prediction accuracy of the model to a certain extent. • Compared with the prediction results of the weight of evidence method and the convolutional neural network, the prediction results of the Siamese network are better, and 7 new gold prospecting areas have been discovered in Zhangjiakou, Hebei Province, China. Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas. [ABSTRACT FROM AUTHOR]

Accurate multi-node load forecasting is the key to the safe, reliable, and economical operation of the power system. However, the dynamic nature of load and the coupling nature of networks are difficult to extract, making consistent and accurate forecasting of node load rather difficult. In this regard, this paper proposes a soft sharing multi-task deep learning method for multi-node load forecasting in the power system. It has the following aspects: (1) Considering the coupling characteristics of the node network, a multi-modal feature module, based on the inception strategy and gated temporal convolutional network (GTCN), is firstly designed to explore the coupling features implied in the node load data. (2) A novel multi-objective neural network model is proposed to achieve simultaneous prediction of multi-node load by integrating the multi-modal feature module and gated recurrent unit (GRU). For sharing the learning information of sub-networks, this paper uses the soft sharing mechanism to capture load features, which can better optimize the prediction task for each node load simultaneously. Load data from the New Zealand distribution network and AEMO are used to compare the proposed model's performance in various scenarios using regression metrics such as mean absolute percentage error (MAPE), Weighted Mean Accuracy (WMA), root mean squared logarithmic error (RMSLE), and Diebold–Mariano (DM). The simulation results show that the proposed method can explore the spatial–temporal coupling characteristics in multi-node load data. Compared with existing state-of-the-art multi-node load prediction methods, our proposed method's MAPE decrease 17.04% and 3.92% in Non-aggregation and Aggregation situations. • A novel deep multi-task learning method is proposed for the multi-node forecasting problem. • An IGTCN module is designed to extract the coupling features from different node loads. • The multi-task deep neural network exhibits the accuracy of multi-node load forecasting. [ABSTRACT FROM AUTHOR]

Short-term ocean waves forecasting requires a high degree of skill and knowledge, as one should observe the available model forecast and real-time measurement and reach a combined estimation. This paper presents a deep learning model providing a short-term wave height prediction derived from recent in-situ measurements and an available mid-range forecast. The model is based of a gated recurrent unit, which is common in time-series forecasting. The model is able to improve significant wave height RMSE by as much as 76% for 1 h forecasts and converge to ∼ 12% improvement for forecasts over 7 h. The model is also shown to be easily transferable to another station and achieves good performance without further training in a "zero-shot" learning process. This model can prove valuable to various off-shore operations, allowing for data-driven decision making instead of skilled human operator and experience-based evaluation. • Short-term wave forecasting using machine learning was able to improve ERA5 RMSE by as much as 76%. • The model uses a GRU neural network with buoy and ERA5 data as input to predict wave heights • The model was transferred to a different location achieving similar results without retraining [ABSTRACT FROM AUTHOR]

• Deep neural network is employed for wind pressure prediction for the first time. • Errors of all taps on roof are exhaustively analysed by multiple error metrics. • The accuracy in roof ridge and corner bay is evidently higher than the literature. • A nested DNNs model is proposed to improve the accuracy in the corner zone. Although the problems of wind pressure prediction on roofs have been studied extensively, the prediction accuracy is still unsatisfactory owing to the limited capacity of shallow learning of artificial neural networks (ANNs), especially in the areas characterized by flow separation. Moreover, the literatures have only conducted error analysis on part of the predicted taps instead of all taps, and have not made precision comparison with related studies. In this paper, the model of deep neural networks (DNNs) with the ability of deep learning are built to predict the mean and root-mean-square (RMS) wind pressure coefficients on low-rise buildings. For quantitatively comparing the presented results with those from the literature, the same buildings from the NIST-UWO database as studied by the literature are also predicted in this work. In order to improve the DNNs model for predicting the corner zone with higher pressure gradients, a nested DNNs model is further proposed by returning the mean coefficients predicted by one DNNs model as the input of another DNNs model for the RMS coefficients. The prediction results of the DNNs model are also compared with those from the methods of random forest (RF) and general regression neural network (GRNN). The study shows that the DNNs model obviously enhances the prediction accuracy around the roof ridge and the corner bay in comparison with the ANNs model. For the mean or RMS coefficients of all the taps on the whole roof, the correlation coefficient between predicted and experimental results exceeds 0.997, the mean-square-error (MSE) is less than 5 %, and the relative errors of>95 % predicted samples are lower than 10 %. The prediction accuracy in the corner zone is further improved by the nested network where the samples within errors less than 5 % are evidently increased and the errors > 20 % disappear. Also, the prediction results from the DNNs model are apparently better than those from RF and GRNN. This study could provide a benchmark for future studies on the prediction of mean or RMS wind pressure coefficients on the roofs. [ABSTRACT FROM AUTHOR]

Trajectory prediction is an essential component of the next generation national air transportation system. Reliable trajectory prediction models need to consider uncertainties coming from multiple sources. Environmental factor is one of the most significant reasons affecting trajectory prediction models and is the focus of this study. This paper propose an advanced Bayesian Deep Learning method for aircraft trajectory prediction considering weather impacts. A brief review of both deterministic and probabilistic trajectory prediction methods is given, with a specific focus on learning-based methods. Next, a deterministic trajectory prediction model with classical deep learning methods is proposed to handle both spatial and temporal information using a nested convolution neural network, recurrent neural network, and fully-connected neural network. Following this, the deterministic neural network model is extended to be a Bayesian deep learning model to consider uncertainties where the posterior distributions of parameters are estimated with variational inference for enhanced efficiency. Both mean prediction and confidence intervals are obtained giving the last on-file flight plans and weather data in the region. The proposed methodology is validated using air traffic and weather data from the Sherlock data warehouse. Data pre-processing procedures for big data analytics are discussed in detail. Demonstration and metrics-based validation are performed during severe convective weather conditions for several air traffic control centers. The results show a significant reduction in prediction variance. A comparison with existing methods is also performed. Several conclusions and future works are given based on the proposed study. • This paper investigates the impact of convective weather on aircraft trajectories. • We formulate the strategic trajectory prediction task into a deep learning framework. • The proposed framework reveals the spatial–temporal relationship in the real data. • Variational Bayes approximation on deep learning achieves uncertainty quantification. [ABSTRACT FROM AUTHOR]

• This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. • The methods tested are concrete Dropouts, deep Ensembles, bayesian neural Networks, deep gaussian Processes, and functional neural processes. • The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). • Good epistemic uncertainty estimates were given using functional neural Processes, deep Ensembles, and deep gaussian processes methods, while concrete dropouts and bayesian neural networks needed tunning for better performance. • The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. Deep Learning methods are widely successful and continue to be applied in new fields, including forecasting for energy time series. However, they lack an indication of their confidence in predictions, which helps evaluate, interpret, and improve the forecasting. This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. The methods tested are Concrete Dropouts, Deep Ensembles, Bayesian Neural Networks, Deep Gaussian Processes, and Functional Neural Processes. For evaluation, two load forecasting scenarios are considered: 1-hour-ahead and 24-hour-ahead load forecasting. The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). As a reference, simple Neural Network and Quantile Regression models are used. Also, the ARIMA model and Persistence model are implemented to compare the results. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Continuous Ranked Probability Score (CRPS) are computed to measure the error performance. Based on endogenous load time series in Germany, the results showed that Concrete Dropout, Deep Ensembles, and Bayesian Neural Networks performed similarly well and better than the reference methods. They also indicated that the simpler Concrete Dropout and Deep Ensembles have an advantage in both speed and convergence time. Good Epistemic Uncertainty estimates were given using Functional Neural Processes, Deep Ensembles, and Deep Gaussian Processes methods, while Concrete dropouts and Bayesian Neural Networks needed tunning for better performance. The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. [ABSTRACT FROM AUTHOR]

Froth Flotation is one of the fundamental processes used in iron ore attainment. It involves various physical–chemical reactions, and its multivariate control is not trivial. Traditionally, plant operators get quality measurements from laboratory analysis of sampled material, which can take hours to be published. In this paper, we present alternatives that eliminate both the necessity to periodically sample material as well as the delay associated with laboratory analysis: a predictive machine learning model working as a soft sensor. Two different classes of models were developed, exploring both deep and shallow approaches of machine learning, with a deep neural network architecture constructed for the particular task at hand. All models yielded accurate predictions, with results favoring the deep neural network when a larger volume of data is available. [ABSTRACT FROM AUTHOR]

• Precipitation up to 3-hour is predicted based on radar echo images of Wuhan station. • ST-LSTM-SA is proposed by 3D convolution, spatiotemporal LSTM and self-attention. • ST-LSTM-SA has a better response to medium and heavy rain events. • ST-LSTM-SA outperforms ConvLSTM and machine learning methods at 1–3 h leads. Accurate and timely short-term forecasting services of precipitation variable are significant for people's lives and property security. The data-driven approaches demonstrate promising performance in the extrapolation of precipitation. In this paper, we proposed a spatiotemporal prediction model, namely the Spatial Temporal Long Short-Term Memory based on the self-attentive mechanism (ST-LSTM-SA). This model enables better aggregation of sequence features inspired by proposed improvements. The encapsulated 3D convolution is developed to fully exploit the short-term spatiotemporal information, and the channel correlation is modeled by self-attention mechanism to further improve representations in the long-term interaction, the effectiveness of which is validated in the ablation study. Comprehensive experiments have been conducted on the radar echo sequence, we successfully predicted future radar reflectivity images for next 3 h with data for previous three hours as inputs in Wuhan, China. Three machine learning methods: multiple linear regression (MLR), support vector regression (SVR) and artificial neural networks (ANN) and deep learning model—ConvLSTM have been chosen as comparative groups to corroborate the nowcasting availability of this model for radar echo extrapolation. Moreover, the supplementary experiment on minute dataset also illustrated the superiority of ST-LSTM-SA. The studies analyzed the forecasting performance in terms of image quality and rainfall error. The experimental results demonstrated the better versatility and performance of ST-LSTM-SA. These conclusions and attempts may provide efficient guidance for precipitation nowcasting in urban areas. [ABSTRACT FROM AUTHOR]

Real-time and accurate traffic state forecasting of urban roads is of great significance to improve traffic efficiency and optimize travel routes. However, future traffic state forecasting is still a challenging issue as it is influenced by several complicated factors including the dynamic spatio-temporal dependencies. Existing models usually consider the dependencies from the road sections with physical connections and ignore the road sections without physical connections. To this end, this paper proposes a deep ensemble neural network (DENN) model to improve the accuracy of urban traffic state forecasting by forming the road sections with high relevance into a virtual graph. To capture the spatio-temporal characteristics efficiently and simultaneously, the DENN integrates the graph convolutional neural network, bidirectional gated recurrent unit network, and a dense layer with attention mechanism into an end-to-end fashion. Validated on two ground-truth urban traffic speed datasets, the DENN model can well fit the nonlinear fluctuation of urban speed and indicate superior performance than the state-of-the-art benchmark methods in terms of prediction precision and robustness. • The virtual network is established by forming the road sections with high relevance into a virtual graph. • The graph convolutional network (GCN) is introduced to mine spatial features of the traffic flow from virtual graph. • Deep ensemble neural network is built by fusing a GCN, Bi-GRU network, and attention model into an end-to-end fashion. • Real-world urban traffic datasets are used to verify the proposed model in terms of prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

This article proposes a two-stage system for the daily energy management of micro-grids (MGs) in the presence of wind turbines, photovoltaic (PV) panels, and electrical energy storage systems (ESSs). Each MG uses historical data to predict its consumers' load demand, wind speed, and solar irradiance in the first stage. In the second stage, the cooperative game method is used to determine the MG's daily dispatch and energy transaction. The paper develops a prediction model using artificial neural network (ANN) and rough neuron water cycle (RNWC) algorithms, called deep learning artificial neural network (DLANN), which is a combination of technology from the artificial neural network and WC algorithm in order to predict uncertain parameters. The above model is implemented in the 33bus power distribution system; the simulation results show that the DLANN method provides more accurate predictions than the ANN method. The results also show that a MG can achieve energy cost savings through an alliance of MGs using the cooperative game approach. Furthermore, analysis of the impact of the ESS on the operation of the MG shows that the absence of the ESS will reduce the power output of the wind turbine. • Providing of a novel deep-learning ANN to develop forecasting model. • Solving multi micro-grid day-ahead scheduling problem using the game theory. • Analyzing the effect of energy storage system on the micro-grids operating cost. • Studying the effect of demand response program on the micro-grids operating cost. [ABSTRACT FROM AUTHOR]

This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward contracts in deregulated energy markets. We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem. Proposed neural network has high expressive power to solve non-linear stochastic forecasting problems with time series including trends, seasonality and significant random fluctuations. At the same time, it is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. We compare our approach against ten baseline methods, including classical statistical methods, machine learning and hybrid approaches, on 35 monthly electricity demand time series for European countries. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias. Code is available here: https://github.com/boreshkinai/nbeats-midterm. [Display omitted] • Our method has superior performance in mid-term load electricity forecasting. • Mean absolute percentage error is reduced by 14%. • Root mean square error is reduced by 3%. • Proposed pinball mean absolute percentage error loss reduces forecasting bias. [ABSTRACT FROM AUTHOR]

• Deep neural network models that jointly predict the next location and arrival time of a vehicle. • Learns sequential patterns in trajectory data using LSTM networks and self-attention mechanism. • Compares the effect of different model structures - hybrid LSTM and sequential LSTM. • Shows the importance of considering travel time in the next location prediction task. This paper aims to incorporate travel time prediction in the next location prediction problem to enable the prediction of the city-wide movement trajectory of an individual vehicle by considering both where the vehicle will go next and when it will arrive. We propose two deep learning models based on long short-term memory (LSTM) neural networks with self-attention mechanism—namely, hybrid LSTM and sequential LSTM. These models capture patterns in location and time sequences in trajectory data and their dependencies to predict next locations and travel times simultaneously. Using Bluetooth vehicle trajectory data from Brisbane, Australia, we compare the prediction performance of the proposed models with several existing approaches including hidden Markov model and other LSTM-based models. The results show that the proposed models produce higher prediction accuracy for both location and time prediction tasks, with the sequential LSTM yielding the best performance. Compared to the conventional next location prediction problem, which considers location sequences only without travel time consideration, the study finds that jointly modelling location and travel time sequences actually improves the next location prediction performance itself, potentially because travel time observations capture the information on traffic conditions in the network, which may affect drivers' location choices. We demonstrate an application of the proposed models in network traffic management, where important locations can be identified to mitigate congestion in a hot-spot by predicting where vehicles come from and go next in an urban road network. [ABSTRACT FROM AUTHOR]