Symmetric Error Research Articles

Extrapolation to the complete basis-set limit in density-functional theory using statistical learning

The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit for periodic crystal structures. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model and a symbolic regression approach using the SISSO model are used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. SISSO outperforms the random forest model for the code. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions. Published by the American Physical Society 2025

Artificial Intelligence Based Hybrid ASFO-ESVM for Load Demand Prediction in Micro Grid Energy Management

Predicting load demand is relevant when used in microgrid energy management systems to address issues such as nonlinear and dynamic consumption data. In this research, the author presents a fusion of Adaptive Sunflower Optimization (ASFO) and Enhanced Support Vector Machine (ESVM) methods to predict the load demand in micro grid environment. The ASFO algorithm enhances the efficiency of the ESVM through a fine-tuning meta-heuristic algorithm based on the sunflower natural organisms. This integration of ASFO and ESVM eliminates many of the drawbacks associated with the basic performance of the task, namely low speed of convergence, overtraining, and the presence of local minima in choosing the parameters. Some of the general parameters used in training and validating the model include load and meteorological data features involving, weather, temporal, load histories are the main contributors in the analysis. Comparisons with other ML algorithm ‘shave been made in respect of relative performance against established methods, such as Random Forest (RF) and Particle Swarm Optimization based with ESVM (PSO-ESVM). The findings infer that lower values of Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) and higher consistency index (d) are yielded by the proposed hybrid ASFO-ESVM model. For instance, even on working days of the week, the precision of the load forecasts was higher with the hybrid model than with the other options. The outcomes do prove that the proposed ASFO-ESVM model is very reliable and precise in its concerning aspect of load demand forecasting as it can be seen in the results obtained for different situations. Relatively, this work estimates a cost effective and feasible method for micro grid energy predictions which can enhance decisions in matters concerning power production, distribution, and control of energy. The study shows how these techniques are relevant towards the complexity and dynamism of the contemporary energy systems.

APPLICATION OF THE DYNAMIC FACTOR MODEL ON NOWCASTING SECTORAL ECONOMIC GROWTH WITH HIGH-FREQUENCY DATA

Economic growth is crucial for planning, yet delayed data releases challenge timely decision-making. Nowcasting offers near-real-time insights using high-frequency indicators (released monthly, weekly, or even daily) to predict low-frequency variables (quarterly or yearly). This study uses high-frequency indicators (monthly), such as stock price changes, air quality, transportation data, financial conditions, and Google Trends, to nowcast quarterly GDP through the Dynamic Factor Model (DFM). The data used span from January 2010 until March 2023, which is split into two: January 2010 until March 2022 for training data and the rest as testing data. Compared to the benchmark Autoregressive Moving Average with Exogenous Variables (ARMAX) model, DFM demonstrates superior accuracy with lower symmetric Mean Absolute Percentage Error (sMAPE). In addition, to evaluate the model performance in nowcasting the GDP across the sector using DFM, the additional metrics, i.e., Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Adjusted R-squared, concluded that in the industrial and transportation sectors results in sufficient nowcasting of GDP, Meanwhile, In the financial sector, the results of the nowcasting GDP give poor estimation results that need improvement.

3D imaging method based on binary encoded fringes with complementary symmetric error diffusion paths

3D imaging method based on binary encoded fringes with complementary symmetric error diffusion paths

A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness.

The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases. The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID-19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet. The hybrid ARIMA-LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%. The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.

Types of refractive errors in a sample of Iraqi children with Intermittent exotropia

Background One of the most common strabismus types in children is intermittent exotropia, which predominantly occurs in children between the ages of 2 to 4 years. It may affect visual development and often coexists with refractive errors. Unlike esotropia, which usually links to hyperopia, intermittent exotropia might have a different relationship with refractive errors and thus lead to myopia. Methods In this cross-sectional retrospective study from August 2021 to December 2023, 179 patients diagnosed with intermittent exotropia were recruited via an outpatient clinic of Najah Al-Quraishi, Baghdad, Iraq. The refractive errors were compared by autorefractometry and retinoscopy after cycloplegic dilation. Data analysis was constructed under the use of IBM SPSS V.26 for the determination of emmetropia, myopia, and hyperopia prevalence. Results Among the 179 patients, emmetropia was the most commonly observed refractive status, present in 68 patients (38%). Low hyperopia and low myopia were also common, with 64 and 40, respectively. A limited number of patients had moderate/high myopia and moderate/high hyperopia; in detail, the prevalence was following: 5% of patients had moderate myopia, while 0.5% of patients suffered from high myopia; symmetrically, 5% were moderate to high hyperopia. From the data collected, a trend emerged for a low refractive error and symmetric refractive error in both eyes. Conclusion Contrasted to prior conventional wisdom about refractive error in strabismus, it established the greater incidence of emmetropia in patients with intermittent exotropia. The findings call for specific management strategies to be applied in this population.

Systematic evaluations of receptor models in source apportionment of particulate solids in road deposited sediments: A practical application for tracking heavy metal sources on urban road surfaces.

Receptor models have been widely used to identify pollution sources in the urban environment. However, evaluating the accuracy of source apportionment results for road deposited sediments (RDS) using these models has not been the focus of previous studies. This study compared canonical receptor models, i.e., positive matrix factorization (PMF), Unmix, chemical mass balance (CMB) and chemical mass-balance based stochastic approach (SCMD) using six synthetic datasets generated from real-world source profiles, and three error evaluation indicators (ie., relative error (RE), relative prediction error (RPE), and symmetric mean absolute percentage error (SMAPE)) were employed. The SCMD model showed more stable and accurate results, with ranges from 8.48 % - 30.76 %, 16.32-32.34 %, and 7.81-24.55 % of RE, RPE, and SMAPE, respectively. SCMD was then applied for tracking Pb, Zn, Cr, Cu, Ni, and Mn on urban road surfaces in Guangzhou, China. The results showed that vehicle exhaust, tire wear, roadside soil, and brake wear contributed 50.15 %, 41.15 %, 6.84 %, and 1.86 % of the mass of particulate solids, respectively; vehicle exhaust contributed more than half of these six heavy metals, particularly Cr and Ni. These findings provide scientific support for the effective selection of appropriate receptor models for source apportionment in RDS.

Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting

This study evaluates the performance of three forecasting models—ARIMA, Prophet, and Glmnet—with the primary objective of equipping the telecommunication industry with effective tools for cellular traffic forecasting. These tools lay the foundation for efficient resource management, cost optimization, and enhanced service delivery. The study begins with dataset description and preparation, followed by the selection of traffic forecasting models, and concludes with performance evaluation based on metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The main contribution of this research is a comprehensive comparison of the three forecasting methods, aiding practitioners and researchers in identifying the best prediction model for specific contexts. The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain. Doi: 10.28991/ESJ-2024-08-06-04 Full Text: PDF

Cloud radon data for earthquake magnitude prediction using machine learning

<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>

Time Series Forecasting using RNN

Time series forecasting is essential across various fields such as finance, economics, meteorology, and healthcare, where accurate predictions are crucial for effective decision- making. Traditional statistical methods often fall short in capturing the intricate patterns and long- term dependencies inherent in time series data, limiting their practical applicability. This research investigates the application of Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), to enhance forecasting accuracy. LSTMs are particularly well-suited for this task due to their ability to process and retain information over extended sequences, allowing them to capture complex temporal relationships that conventional methods might overlook. This work employs a comprehensive approach that includes advanced data preprocessing, feature engineering, and model architecture design, combined with meticulous hyperparameter tuning to optimize performance. The effectiveness of the LSTM- based approach is evaluated using the M4 competition dataset, which is widely recognized for its complexity and diversity in time series data. The results demonstrate that the optimized LSTM model consistently outperforms traditional statistical methods. Specifically, it shows superior accuracy in capturing both short-term fluctuations and long- term trends. Performance metrics, including Mean Absolute Scaled Error (MASE) and Symmetric Mean Absolute Percentage Error (sMAPE), reveal significantly lower error rates with the LSTM model compared to conventional approaches. The LSTM model’s proficiency in predicting long-term trends further highlights its effectiveness in practical time series forecasting scenarios. Overall, LSTMs prove to be a robust tool for time series forecasting, offering substantial improvements over traditional methods by effectively leveraging long-term dependencies in data. These models are especially valuable in fields like finance, economics, and healthcare. Future studies could aim to further optimize LSTM models and explore their application to other complex datasets, pushing the boundaries of this field even further.

Forecasting the relative abundance of Aedes vector populations to enhance situational awareness for mosquito control operations.

Aedes-borne diseases represent a major public health threat and mosquito control operations represent a key line of defense. Improving the real-time awareness of mosquito control authorities by providing reliable forecasts of the relative abundance of mosquito vectors could greatly enhance control efforts. To this aim, we developed an analytical tool that forecasts Aedes aegypti relative abundance 1 to 4 weeks ahead. Forecasts were validated against mosquito surveillance data (2,760 data points) collected over multiple years in four jurisdictions in the US. The symmetric absolute percentage error was in the range 0.43-0.69, and the 90% interquantile range of the forecasts had a coverage of 83-92%. Our forecasts consistently outperformed a reference "naïve" model for all analyzed study sites, forecasting horizon, and for periods with medium/high Ae. aegypti activity. The developed tool can be instrumental to address the need for evidence-based decision making.

MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.

The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual influence among multiple stations caused by natural environmental factors, e.g., air circulation. Moreover, the existing methods are mostly short-term predictions and perform poorly in long-term forecasting. In this paper, we propose MTLPM, i.e., a spatio-temporal graph neural network model based on an encoder-decoder architecture, which fully exploits the spatial dynamic patterns and long-term dependencies. Firstly, we adopt a message passing mechanism combined with spatial features and complex environmental factors (e.g., temperature, humidity, and wind direction) to update station data, capturing real-time spatial dynamic information. Secondly, we adopt the Multi-head ProbSparse Self-attention to extract temporal features, learning the long-term dependency relationships among the data. Finally, we adopt a generative one-step decoder structure to simultaneously forecast the data for multiple stations over a long period. We conducted experiments on both the project dataset and the publicly available dataset. Compared to existing state-of-the-art methods, MTLPM achieved an average reduction of approximately 1.6 in mean absolute error (MAE) and approximately 0.02 in symmetric mean absolute percentage error (SMAPE) in predicting results. The relevant source code is publicly available on GitHub1.

Deep Learning-Enabled De-Noising of Fiber Bragg Grating-Based Glucose Sensor: Improving Sensing Accuracy of Experimental Data

This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability in experimental data, we initially employ seasonal decomposition using moving averages (SDMA) statistical models to filter out redundant data points. Subsequently, sequential DL models extrapolate the normalized transmittance (Tn) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve Tn sequence prediction accuracy. Evaluation metrics demonstrate its superior performance, including a root mean square error of 1.73 ± 0.05, a mean absolute error of 1.20 ± 0.04, and a symmetric mean absolute percentage error of 2.22 ± 0.05, among others. Additionally, our novel minima difference (Min. Dif.) metric achieves a value of 1.08 ± 0.46, quantifying wavelength for the global minima within the Tn sequence. The composite attention mechanism in the AttentiveSpecExLSTM adeptly captures both high-level and low-level dependencies, refining the model’s comprehension and guiding informed decisions. Hierarchical dot and additive attention within this model enable nuanced attention refinement across model layers; dot attention focuses on high-level dependencies, while additive attention fine-tunes its focus on low-level dependencies within the sequence. This innovative strategy enables accurate estimation of the spectral width (full-width half maxima) of the Tn curve, surpassing raw data’s capabilities. These findings significantly contribute to data quality enhancement and sensor performance analysis. Insights from this study hold promise for future sensor applications, enhancing sensitivity and accuracy by improving experimental data quality and sensor performance assessment.

The short-term wind power prediction based on a multi-layer stacked model of BO[sbnd]CNN-BiGRU-SA

Wind power generation is influenced by various meteorological factors, exhibiting significant volatility and unpredictability. This variability presents considerable challenges for accurate wind power forecasting. In this study, we propose an innovative method for short-term wind power prediction that integrates a Bayesian-optimized Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-Attention Mechanism (SA) within a multi-layer architecture. Initially, we preprocess features using Pearson correlation analysis and input them into the CNN to investigate complex nonlinear spatial relationships among multiple feature variables and the current load. Subsequently, the BiGRU captures long-term dependencies from both forward and backward time sequences. Finally, we implement the Self-Attention Mechanism to weigh the features and generate the predicted wind power. We optimize the model's numerous hyperparameters utilizing a Bayesian algorithm. Through comparative ablation experiments with varying time segment lengths on wind farm datasets from four regions, our method significantly outperforms 11 models, including Long Short-Term Memory (LSTM), and surpasses several state-of-the-art (SOTA) prediction models, such as iTransformer, PatchTST, Non-stationary Transformers, TSMixer, and DLinear. The highest coefficient of determination (R²) achieved was 0.981, with the Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) decreasing by 11.22 % to 62.04 % compared to other models. The results demonstrate the predictive accuracy and generalization performance of our proposed model.

Types of refractive errors in a sample of Iraqi children with Intermittent exotropia

Background One of the most common strabismus types in children is intermittent exotropia, which masks the deviation of one eye outward, mostly in children aged 2-4 years. It may affect visual development and often coexists with refractive mistakes. Unlike esotropia, which usually links to hyperopia, intermittent exotropia might have a different relationship with refractive errors and thus lead to myopia. Methods In this cross-sectional study from August 2021 to December 2023, 179 patients diagnosed with intermittent exotropia were recruited via an outpatient clinic of Najah Al-Quraishi, Baghdad, Iraq. The refractive errors were compared by autorefractometry and retinoscopy after cycloplegic dilation. Data analysis was constructed under the use of IBM SPSS V.26 for the determination of emmetropia, myopia, and hyperopia prevalence. Results The most expected refractive error among the 179 patients was emmetropia, which occurred in 68 patients (38%). Low hyperopia and low myopia were also common, with 64 and 40, respectively. A limited number of patients had moderate/high myopia and moderate/high hyperopia; in detail, the prevalence was following: 5% of patients had moderate myopia, while 0.5% of patients suffered from high myopia; symmetrically, 5% were moderate to high hyperopia. From the data collected, a trend emerged for a low refractive error and symmetric refractive error in both eyes. Conclusion Contrasted to prior conventional wisdom about refractive error in strabismus, it established the greater incidence of emmetropia in patients with intermittent exotropia. The findings call for specific management strategies to be applied in this population.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data

In the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud radon data. We present an analysis of the tele-monitoring system, data collection methods, and the application of regression-based machine learning algorithms. Utilizing a comprehensive dataset spanning 30 training instances and 105 test instances, the study evaluates multiple metrics to ascertain the efficacy of the prediction models. Our findings reveal that the linear regression approach yields the best earthquake magnitude prediction method, with the lowest values across multiple evaluation metrics: standard deviation 0.40, mean absolute error (MAE) 0.30, mean absolute percentage error (MAPE) 6%, root mean square error (RMSE) 0.52, mean squared error (MSE) 0.28, symmetric mean absolute percentage error (SMAPE) 0.06, and conformal normalized mean absolute percentage error (cnSMAPE) 0.97. Additionally, we discuss the implications of the research results and the potential applications in enhancing existing earthquake prediction methodologies.

Indonesian Inflation Forecasting with Recurrent Neural Network Long Short-Term Memory (RNN-LSTM)

This study forecasted inflation in Indonesia using the Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) model, ideal for nonlinear, complex time series data. It evaluated the effects of different activation functions, such as Logistic, Gompertz, and Hyperbolic Tangent (tanh); and weight update methods, such as Stochastic Gradient Descent (SGD) and Adaptive Gradient (AdaGrad) on RNN-LSTM performance. Monthly inflation data from January 2005 to December 2023 underwent preprocessing, including normalization and autoregressive lag-based input selection. Model accuracy was assessed with Root Mean Squared Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). The findings indicated that the RNN-LSTM model with the logistic activation function and SGD optimization achieved the highest accuracy, outperforming traditional models such as Exponential Smoothing (ETS), Autoregressive Integrated Moving Average (ARIMA), Feedforward Neural Network (FFNN), and Recurrent Neural Network (RNN). Additionally, optimal learning rate and epoch values were identified, enhancing model stability and precision. In conclusion, the study confirms that the RNN-LSTM model is effective for inflation forecasting when optimized with specific activation functions and optimization methods. It recommends further exploration of neuron configurations and alternative models, such as the Gated Recurrent Unit (GRU), to improve forecast accuracy.

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Prediction OPEC oil price utilizing long short-term memory and multi-layer perceptron models

The present study undertakes a comprehensive assessment of two predictive models, namely Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP), with a specific emphasis on their effectiveness in predicting oil prices, particularly those of the Petroleum Exporting Countries (OPEC). In this study, three fundamental statistical measures are utilized: The Symmetric Mean Absolute Percentage Error (SMAPE), the Mean Squared Error (MSE), and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the LSTM model regularly surpasses the MLP model in the three benchmarks. In particular, the LSTM model demonstrates lower values for SMAPE, MSE, and MAPE, indicating higher prediction accuracy. The decreased error scores linked to the LSTM model highlight its improved capacity for precise oil price prediction in comparison to the MLP model. These results signify a notable progress in the use of machine learning techniques for predicting OPEC oil prices. Moreover, this study provides invaluable perspectives for OPEC management, policymakers, and organizations focused on oil price fluctuations, therefore contributing to the wider endeavour of enhancing the stability and economic sustainability of the oil pricing system in OPEC countries. The consequences of the study include the promotion of a pricing system that facilitates the achievement of economic and social development goals in these countries.