Accurate Short-term Load Forecasting Research Articles

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy.

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.

Predictive models for short-term load forecasting in the UK's electrical grid.

There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK's power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.

Short-term power load forecasting method based on Bagging-stochastic configuration networks.

Accurate short-term load forecasting is of great significance in improving the dispatching efficiency of power grids, ensuring the safe and reliable operation of power grids, and guiding power systems to formulate reasonable production plans and reduce waste of resources. However, the traditional short-term load forecasting method has limited nonlinear mapping ability and weak generalization ability to unknown data, and it is prone to the loss of time series information, further suggesting that its forecasting accuracy can still be improved. This study presents a short-term power load forecasting method based on Bagging-stochastic configuration networks (SCNs). First, the missing values in the original data are filled with the average values. Second, the influencing factors, such as the weather- and week-type data, are coded. Then, combined with the data of influencing factors after coding, the Bagging-SCNs integration algorithm is used to predict the short-term load. Finally, by taking the daily load data of Quanzhou City, Zhejiang Province as an example, the program of the abovementioned method is compiled in Python language and then compared with the long short-term memory neural network algorithm and the single-SCNs algorithm. Simulation results show that the proposed method for medium- and short-term load forecasting has a high forecasting accuracy and a significant effect on improving the accuracy of load forecasting.

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Optimized short-term load forecasting in residential buildings based on deep learning methods for different time horizons

The aim of this paper is to develop machine learning based framework to short-term load forecasting with high accuracy for residential building. The purpose is to develop a predictive model that assists energy companies in achieving a balance between energy consumption and generation by effectively managing the energy demand of consumers. The originality of this paper lies in two main parts. First, it analyzes effective relevant features such as time, calendar, and weather using a correlation matrix. Next, an optimized eXtreme gradient boosting model is employed to select key features, reducing the complexity of the training model. The second part proposes an intelligent parallel structure that utilizes gated recurrent units and convolutional neural networks for short-term load forecasting in different resolutions with minimal errors. The precise selection of hyperparameters significantly influences error prediction and accuracy. The metaheuristic-search-based algorithm, particle swarm optimization, is applied to find the optimal configuration for tunable parallel proposed model hyperparameters. The main results of this study demonstrate that the intelligent predictive model performs better than the latest models discussed in recent literature. The evaluation was conducted on a real-time time series dataset called Mashhad data. The model achieved impressive results in terms of standard metrics such as root mean square error of 44.28, mean absolute error of 29.32, mean absolute percentage error of 3.11 %, and R2 of 0.9229 % for the next 24 h. The benefits of this novel model play a crucial role in accurate short-term load forecasting, as well as in the decision-making and operations of power companies and smart grid.

Short-Term Load Forecasting Based on CNN-Bilstm Considering Load Time-Varying Trend Mapping

Background:: Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems. Objective:: In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M). Methods:: Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting. Result:: The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method. Conclusion:: With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.

Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

The significance of accurate short-term load forecasting (STLF) for modern power systems’ efficient and secure operation is paramount. This task is intricate due to cyclicity, non-stationarity, seasonality, and nonlinear power consumption time series data characteristics. The rise of data accessibility in the power industry has paved the way for machine learning (ML) models, which show the potential to enhance STLF accuracy. This paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR), examining both standalone and integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, and EWT. Through Automated Machine Learning (AutoML), these models are integrated and their hyperparameters optimized, predicting each load signal component using data from two sources: The National Operator of Electric System (ONS) and the Independent System Operators New England (ISO-NE), boosting prediction capacity. For the 2019 ONS dataset, combining EWT and XGBoost yielded the best results for very short-term load forecasting (VSTLF) with an RMSE of 1,931.8 MW, MAE of 1,564.9 MW, and MAPE of 2.54%. These findings highlight the necessity for diverse approaches to each VSTLF problem, emphasizing the adaptability and strength of ML models combined with signal decomposition techniques.

Time series forecasting of electrical energy consumption using deep learning algorithm

Energy consumption forecasting is an operation of predicting the future energy consumption of electrical systems using previous or historical data. The Long Short-term Memory (LSTM) Model; a deep learning model was used in this project to analyze the Short-term consumption forecast performance. This was carried out by using an energy consumption dataset obtained from the Transmission Company of Nigeria (TCN) Benin City regional 132/33KV transmission station. The dataset were daily load readings recorded in the half-hourly format from August to December 2021. The model was used to demonstrate the feasibility of generating an accurate short-term load forecast for the case study despite the peculiarity and insufficiency of the energy consumption readings. Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the statistical evaluation metrics used. The approach produces exceptional levels of accuracy, with MAPE of 0.010 and RMSE of 19.759 for a 100 time-step. The findings imply that the LSTM model can make accurate predictions with minimal error, and this Deep learning model may be a useful tool for short-term forecasting demand. This finding serves as a baseline for future research in this field of study and power system planning.

Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting.

Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks

The accurate short-term electric load forecasting (STLF) is critical for the safety and economical operation of modern electric power systems. Recently, the graph neural network (GNN) has been applied in STLF and achieved impressive success via utilizing spatial dependency between residential households to improve STLF. However, GNN based forecasting models require a large amount of training data to learn reliable forecasting models. For a newly built residential neighbourhood, the historical electric load data might be insufficient for the training of GNNs. Meanwhile, we can learn GNN based models on other areas, referred to as the source domains, with abundant data. In this paper, we propose to reuse the knowledge learned on the source domains to assist the model learning for an area that only a limited amount of data is available, referred to as the target domain. Specifically, we propose an attentive transfer framework to ensemble the GNN models trained from source domains and the GNN model trained on the target domain. The proposed framework can dynamically assign weights to different GNN based models based on the input data. Extensive experiments have been conducted on real-world datasets and shown the effectiveness of the proposed framework on different scenarios.

Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model

Accurate short-term load forecasting is the key to ensuring smooth and efficient power system operation and power market dispatch planning. However, the nonlinear, non-stationary, and time series nature of load sequences makes load forecasting difficult. To address these problems, this paper proposes a short-term load forecasting method (EPT-VMD-TCN-TPA) based on the hybrid decomposition of load sequences, which combines ensemble patch transform (EPT), variational modal decomposition (VMD), a temporal convolutional network (TCN), and a temporal pattern attention mechanism (TPA). In which, the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load series are extracted using EPT, and then the Re(t) component is decomposed into intrinsic modal function components (IMFs) of different frequencies using VMD. The Tr(t) and IMFs components of the fused meteorological data are predicted separately by the TCN-TPA prediction model, and finally, the prediction results of each component are reconstructed and superimposed to obtain the final predicted value of the load. In addition, experiments after reconstructing each IMF component according to the fuzzy entropy (FE) values are discussed in this paper. To evaluate the performance of the proposed method in this paper, we used datasets from two Areas of the 9th Mathematical Modeling Contest in China. The experimental results show that the predictive precision of the EPT-VMD-TCN-TPA model outperforms other comparative models. More specifically, the experimental results of the EPT-VMD-TCN-TPA method had a MAPE of 1.25% and 1.58% on Area 1 and Area 2 test sets, respectively.

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.

Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

Accurate short-term load forecasting (STLF) is essential to improve secure and economic operation of power systems. In this paper, a hybrid STLF model using the conditional generative adversarial network (CGAN) with convolutional neural network (CNN) and semi-supervised regression is proposed to improve the accuracy of STLF. Firstly, a conditional label matrix with relevant factors is constructed as the conditional labels of CGAN. The grey weighted correlation method is applied to generate high-quality similar days as one of the labels. The input data with conditional labels and load time series are decomposed into several sub-modes by the variational mode decomposition (VMD), which transforms the load forecasting into several sub-forecasting. Then, the CGAN generator is to capture the internal feature of each mode with the CNN and generate fake samples, while the CGAN discriminator is modified with a semi-supervised regression layer to extract the nonlinear and dynamic behaviors of the dataset and perform precise STLF. The final forecasting results are obtained by aggregating the results of all sub-mode. The generator and discriminator of the CGAN form a min–max game to improve the sample generation ability and reduce forecasting errors. The simulation results show that the STLF accuracy with the proposed model is significantly improved.

MFAMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting

Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network (CNN) or long short-term memory (LSTM) model to realize load forecasting. For the multi-factor input sequence, the existing methods cannot obtain multi-scale features of the time series and the important parameters of the multi-factor, resulting in low accuracy and robustness. To address these problems, a multi-scale feature attention hybrid network is proposed, which uses LSTM to extract the time correlation of the sequence and multi-scale CNN to automatically extract the multi-scale feature of the load. This work realizes the integration of features by constructing a circular network. In the proposed model, a two-branch attention mechanism is further constructed to capture the important parameters of different influencing factors to improve the model’s robustness, which can make the network to obtain effective features at the curve changes. Comparative experiments on two open test sets show that the proposed multi-scale feature attention mixture network can achieve accurate short-term load forecasting and is superior to the existing methods.

A Hybrid Short-Term Load Forecasting Approach for Individual Residential Customer

This article proposes a hybrid method (HM) to improve the accuracy of short-term individual residential load forecasting. The HM includes an ensemble model (EM), deep ensemble model (DEM), and thermal dynamic model expressed by resistance-capacitance (RC). The EM consists of three predictors of support vector machine (SVM), back propagation neural network (BPNN), and generalized regression neural network (GRNN). The genetic algorithm (GA) is used to optimize SVM and BPNN to enhance their performance. The DEM includes multiple bi-directional long-short term memory (Bi-LSTM) networks. The Bayesian algorithm (BA) is used to optimize the hyperparameters of the Bi-LSTM. The outputs of individual predictors are aggregated using an optimal trimmed algorithm. At first, the total load is separated into the heater and air conditioning (HAC), and non-HAC loads. Then, the RC model is presented to predict the indoor temperature, which integrates outdoor weather and less HAC historical data as the input of the EM to forecast the HAC load. After that, non-HAC loads are further divided into electric lighting and other loads. A daylight equation is used to calculate the illuminance, which is combined with less lighting historical data as the input of DEM to predict electric lights usage. Then, other loads are captured by DEM through less historical data. Finally, the total load is obtained by combining the predicted HAC and non-HAC loads. The datasets from the UMass Smart Microgrid and Flexhouse projects are used to test the proposed method. The comparison with existing models proves that the presented model can provide accurate short-term individual load forecasting.

Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration

The increasing penetration of photovoltaic has been reshaping the electricity net load curve, which has a significant impact on power system operation and short-term dispatch scheduling. Accurate short-term net load forecasting is essential to ensure reliable and economical operations of a power system. Nonetheless, most of the existing net load forecasting approaches are mostly focused on net load forecasting at household, distribution or microgrid level, but not at grid system-wide level. They also suffer from low accuracy due to the presence of uncertainties on high-frequency fluctuations in the net load. This paper proposes a new improved two-stage net load forecasting method at grid system-wide level. Firstly, it contributes to eliminate the high-frequency components with insignificant amount of energy from the original net load by using the “Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-ICEEMDAN” technique. Then, net load decomposition outcomes form the inputs of a computationally efficient and accurate “Long Short-Term Memory-LSTM” network algorithm to produce an accurate day-ahead forecasting, which lays out the foundation of day-ahead power dispatch scheduling. The superiority of the suggested algorithm was confirmed by comparing the obtained results against five other algorithms that use different empirical based decomposition techniques along with Back Propagation (BP) or LSTM. Statistical metrics, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were computed to show the model accuracy. The validity of the proposed method is verified by the net load data of “South West Interconnected System” power network in Western Australia, which refers to the total demand on the conventional generators made up of the consumers’ actual demand plus system losses, minus the solar power harnessed by the rooftop PV panels installed within the grid system. Achieving a very high day-ahead net load forecasting accuracy of 96.67% confirms our hypothesis on ICEEMDAN’s capability to decompose the net load carefully into different meaningful components.

Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network

Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to predict the characteristic components, and finally obtain the prediction results, and complete the short-term load forecasting. Taking the real data of a company as an example, this paper compares the combined model CEEMD–SSA–GRU with EMD–SSA–GRU, SSA–GRU, and GRU models. Experimental results show that this model has better prediction effect than other models.

Load2Load: Day-ahead load forecasting at aggregated level

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements.In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been evaluated and compared on two datasets with different temporal resolutions and features. An additive ensemble method as well as a selective ensemble method that selects the outputs of different forecasters in an hourly manner are proposed. Moreover; a modified sequential forward feature selection algorithm is proposed, resulting in better performance with a much smaller number of features.Numerical results show that the proposed Load2Load architecture has a competing performance compared to other advanced forecasters. When used together with the proposed ensemble methods, Load2Load can significantly improve the forecasting performance. The proposed feature selection algorithm results in better performance for the majority of the cases while reducing the dimensionality. According to the results with two different datasets; the proposed methods are shown to be robust to temporal resolutions, feature types and sequence lengths.