Approach For Short-term Prediction Research Articles

An EEMD-EWT-LSTM-based short-term prediction approach for inbound metro ridership

An EEMD-EWT-LSTM-based short-term prediction approach for inbound metro ridership

Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach.

Remote Geotechnical Monitoring of a Buried Oil Pipeline

Extensive but remote oil and gas fields in Canada and Russia require extremely long pipelines. Global warming and local anthropogenic effects drive the deepening of seasonal thawing of cryolithozone soils and enhance pathological processes such as frost heave, thermokarst, and thermal erosion. These processes lead to a reduction in the subgrade capacity of the soils, causing changes in the spatial position of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the daily temperatures of unevenly heated soils along pipeline routes. However, they are confronted with the problem of separating anthropogenic heat losses from seasonal temperature fluctuations. To highlight heat losses, we propose a short-term prediction approach to a transformed multidimensional dataset. First, we define the temperature intervals according to the classification of permafrost to generate additional features that sharpen seasonal and permafrost conditions, as well as the timing of temperature measurement. Furthermore, linear and nonlinear uncorrelated features are extracted and scaled. The second step consists of selecting a training sample, learning, and adjusting the additive regression model. Forecasts are then made from the test sample to assess the accuracy of the model. The forecasting procedure is provided by the three-component model named Prophet. Prophet fits linear and nonlinear functions to define the trend component and Fourier series to define the seasonal component; the third component, responsible for the abnormal days (when the heating regime is changed for some reason), could be defined by an analyst. Preliminary statistical analysis shows that the subsurface frozen soils containing the oil pipeline are mostly unstable, especially in the autumn season. Based upon the values of the error metrics, it is determined that the most accurate forecast is obtained on a three-month uniform time grid.

A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.

A probabilistic approach for short-term prediction of wind gust speed using ensemble learning

Strong winds could cause train derailment and truck rollover which may result in service interruption, serious injury, and even loss of life. The wind-induced accident is highly related to the maximum value of short-term wind speed, thus highlighting the importance of regulating the vehicle velocity based on wind gusts. Accurate prediction of wind gusts is essential to control the vehicle velocity ahead of time, thereby reducing the risk of accidents. The majority of existing approaches focus on the prediction of mean wind speed. In contrast, fairly limited research applies the machine learning model to forecast wind gusts with strong time-varying characteristics and volatility. In this study, a probabilistic approach is presented to forecast wind gusts using ensemble learning. The ensemble model includes three machine learning models, namely, random forest (RF), long-short term memory (LSTM), and Gaussian process regression (GPR) model. The proposed probabilistic approach allows for the quantification of uncertainty in prediction of wind gusts. The feasibility of the ensemble model is illustrated by using the field wind measurements acquired from a long-span cable-stayed bridge. Compared to the persistence, RF, LSTM, GPR, averaging, and gradient boosting decision tree models, the proposed ensemble model exhibits higher accuracy and generalization performance.

Multi-model Short-term Prediction Schema for mHealth Empowering Asthma Self-management

Ambient intelligence and machine learning techniques are widely proposed by various eHealth and mHealth applications for home-care and self-management of various chronic health conditions. Their adoption for self-management of asthma, a multifactorial chronic disease, requires evaluation and validation in a real-life setups along with optimization at patient level to personalize predictions with respect to asthma control status and exacerbation risk. The current work proposes a novel short-term prediction approach for asthma control status, considering training of multiple classification models for each monitored parameters along with necessary pre-processing methods to enhance robustness and efficiency. The machine learning algorithms considered in this study are the Support Vector Machines, the Random Forests, AdaBoost and Bayesian Network. The Random Forests and Support Vector Machines classifiers demonstrated overall superior performance for the case studies (models) considered.

A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization.

This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.

Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction

Wind energy is one of the emerging sustainable sources of electricity. Wind is intermittent in nature. The typical grid operation of wind energy is complex. The significance of wind energy generation and integration with the grid is increasing day by day. An accurate wind speed forecasting method will help the utility planners and operators to meet the balance of supply and demand by generating wind energy. In this paper, a statistical-based wind speed prediction is implemented without utilizing the numerical weather prediction inputs. This analytical study proposes a hybrid short-term prediction approach that can successfully preprocess the original wind speed data to enhance the forecasting accuracy. The most efficient signal decomposition algorithm, Ensemble Empirical Mode Decomposition is used for preprocessing. This ensemble empirical mode decomposition technique decomposes the original wind speed data. Each decomposed signal is regressed to forecast the future wind speed value by utilizing the Adaptive Wavelet Neural Network model. The proposed hybrid approach is subsequently investigated with respect to the wind farm of South India. The results from a real-world case study in India are reported along with comprehensive comparison. The prediction performance delivered high accuracy, less uncertainty and low computational burden in the forecasts attained. The developed hybrid model outperforms the six other benchmark models such as persistence method, back propagation neural network, radial basis function neural network, Elman neural network, Gaussian regression neural network, and wavelet neural network.

Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach

As the crucial foundation of distributed energy management systems, a reliable prediction of dynamic energy consumption is urgently needed in the building sector. Advances in metering technology and machine learning methods provide both opportunities and challenges. In particular, a clustering-based hybrid model is established in this paper to predict the day-ahead hourly electric demand in hotel buildings, which were initially classified as the non-stationary operated buildings (NOB) due to the irregular electric temporal features. Specifically, with multi-dimensional attributes of 100days considered by fuzzy c-means (FCM) clustering, similar days and hours were extracted stepwise into several homogeneous groups. Afterwards, the online modified predictor was established by the combination of support vector regression (SVR) and wavelet decomposition, taking the corresponding training samples extracted by FCM as the input. Clustered results of similar days testified the clear identification of stationary subsequences (Group IV) and non-stationary subsequences (Group I). Based on the validation, it can be concluded that the introduction of days-clustering can always improve the accuracy of day-ahead prediction, while the recognition of similar hours is necessary only for non-stationary subsequences. Finally, this paper offers an online short-term prediction approach for model-based energy management, based on the limited and fluctuating historical records.

A new modeling approach for short-term prediction of occupancy in residential buildings

Occupancy models are necessary towards design and operation of smart buildings. Developing an appropriate algorithm to predict occupancy presence will allow a better control and optimization of the whole building energy consumption. However, most previous studies of development of such model only focus on commercial buildings. The occupancy model of residential houses are usually based on Time User Survey data. This study focuses on providing a unique data set of four residential houses collected from occupancy sensors. A new inhomogeneous Markov model for occupancy presence prediction is proposed and compared to commonly used models such as Probability Sampling, Artificial Neural Network, and Support Vector Regression. Training periods for the presence prediction are optimized based on change-point analysis of historical data. The study further explores and evaluates the predictive capability of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-hour ahead, and 24-hour ahead forecasts. The spatial-level comparison is additionally conducted by evaluating the prediction accuracy at both room-level and house-level. The final results show that the proposed Markov model outperforms the other methods in terms of an average 5% correctness with 11% maximum difference in 15-min ahead forecast of the occupancy presence. However, there is not much differences observed for 24-hour ahead forecasts.

An Approach for Short-Term Prediction on Time Series from Parameter-Varying Systems

Time series prediction is a very important problem in many applications and the current prediction techniques are nearly all based on the Takens' embedding theorem. Many realistic systems are parameter-varying systems, and the embedding theorems are invalid, predicting the behavior of parameter-varying systems is more difficult. This paper proposes the novel prediction techniques for parameter-varying systems reconstruction, which are based on wavelet neural network (WNN) and multiwavelets neural network (MWNN). These techniques absorb the advantages of high resolution of wavelet and learning of neural networks. The significant improvement is that the error's functions of both networks are convex, and the problem of poor convergence and undesired local minimum can be solved remarkably. Ikeda time series generated by the parameter-varying systems is adopted to check the prediction performance of the proposed models. The numerical experiments show that the three proposed models are feasible, MWNN has the top performance, and WNN could lead the better results than NN in the prediction of the parameter-varying systems.

Multifractal Description of Road Traffic Structure

In this work, we study the structure of road traffic with the help of fractal and multifractal tools. Using classical models of traffic that lead to a Burgers’ equation, and recent results on the solutions of this equation when the initial conditions are scaling, we predict that, under some circumstances, the traffic can possess a multifractal structure similar to those of multiplicative processes. We verify this behavior on ten minute data of traffic flows. We then propose a approach for short-term prediction using some multifractal parameters.