Multivariable short-term load forecasting in Nigerian microgrids using Bayesian regularization and bi-long short-term memory models

COVID-19 first appeared in China in December 2019. In Türkiye, the first case was seen on March 11, 2020. Since the emergence of COVID-19, it has rapidly affected the world and caused the death of many people. The rapid spread of the virus around the world and the fact that it poses a global threat have made it mandatory for the authorities to take quick measures and decisions. For this reason, the diagnosis, treatment, and prediction of the number of cases of COVID-19 disease is a vital issue. In this paper, daily deaths, daily cases, cumulative deaths, and cumulative case numbers were predicted using Türkiye's COVID-19 data. The prediction analysis for COVID-19 was conducted employing artificial intelligence (AI) techniques, specifically Stacked Long-Short Term Memory (Stacked-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Artificial Neural Network (ANN) methods. Hyperparameter optimization using the Gray Wolf Optimizer Algorithm (GWO) was applied to optimize these models. To assess the accuracy and efficacy of prediction models, various performance metrics were employed, including Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R^2 Score), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Explained Variance Score (EVS). The assessment of the accuracy and efficacy of the pandemic prediction models involved comparing the predicted data values with the actual values. Based on this analysis, the prediction model that exhibited the highest degree of similarity to the actual values was determined to be the most reliable and effective.

In one-dimensional (1D) geomechanical modeling, precise predictions are essential for ensuring wellbore stability, defining safe mud weight windows, and determining hydraulic fracturing pressure ranges. However, accurately estimating geomechanical parameters, especially in data-limited environments, remains a significant challenge. This study addresses these challenges by integrating advanced machine learning algorithms with well log data from two wells in one of Iran’s hydrocarbon fields. The dataset includes shear wave velocity (Vs) logs, static Young’s modulus (Est) samples, uniaxial compressive strength (UCS) samples, internal friction angle ( φ ) measurements, and pore pressure (PP) samples. To tackle the challenge of predicting Vs with high precision, three sophisticated algorithms were employed: Bidirectional Long Short-Term Memory (BiLSTM), BiLSTM+Transformer, and a hybrid model (CBiT) combining 1D Convolutional Neural Network (CNN), BiLSTM, and Transformer mechanisms. Feature selection using Pearson correlation and Mutual Information and SHapley Additive exPlanations (SHAP) analysis identified the most influential parameters for each algorithm. The models were evaluated against traditional empirical methods using statistical metrics, such as mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2). The results revealed that the machine learning models significantly outperformed the empirical methods, with the hybrid CBiT model showing superior accuracy, robustness, and generalization capability, establishing a new benchmark in predictive performance for shear wave velocity due to its ability to capture complex nonlinear relationships and long-term dependencies. Particle Swarm Optimization (PSO) was applied to further optimize model hyperparameters, achieving R2 values of 0.9989 (training) and 0.9985 (testing), with RMSE for unseen data reduced to 0.0269 and R2 reaching 0.9973. Dynamic elastic parameters, including Young’s modulus, bulk modulus, and shear modulus, were calculated using the optimized model. Strong correlations between dynamic and static properties, such as dynamic Young’s modulus and UCS, were extrapolated across the entire depth range of both wells. The study also introduced a novel approach to estimate internal friction angle using Plumb’s correlation and laboratory data, alongside improved pore pressure predictions based on Eaton’s empirical method. Finally, vertical and horizontal stresses were calculated using poroelastic equations for comprehensive stress modeling. This study demonstrates how the integration of hybrid machine learning algorithms, advanced optimization techniques, and rigorous data analysis can significantly improve the accuracy, reliability, and practical applicability of 1D geomechanical models, providing a robust and efficient framework for subsurface analysis and offering substantial advantages over conventional approaches.

With the rapid development of electric vehicles and renewable energy storage systems, the estimation of lithium-ion battery State of Health (SOH) has become increasingly important. Effective SOH monitoring not only extends battery lifespan but also enhances the overall efficiency and reliability of the system. This paper presents SOH method that combines the Kolmogorov-Arnold Network (KAN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. First, features are extracted from historical data, resulting in 14 features. The features are then categorized into two types: one type consists of noisy features, and the other includes features with anomalous measurement data. The Variational Mode Decomposition (VMD) parameters are adjusted for each category, and the processed features are used as indirect features. Three different methods (LSTM BiLSTM and GRU) and three hybrid models (LSTM-KAN BiLSTM-KAN and GRU-KAN) are employed to establish SOH estimation models. The performance of the six models is compared and analyzed. Results indicate that models combined with the KAN network outperform their baseline counterparts, with the BiLSTMKAN model exhibiting the highest estimation accuracy compared to the other models. It achieves lower Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), significantly enhancing prediction accuracy and efficiency.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

This paper presents a model that combines mode decomposition approaches with a bi-directional long short-term memory (BiLSTM) attention mechanism and a transformer (AMT) to predict the concentration level of ozone (O3) in Johannesburg, South Africa. Johannesburg is a densely populated city and the industrial and economic hub of South Africa. Being the industrial hub, air pollution is a major concern as it affects human health. Using air pollutants and meteorological datasets, a model was proposed that uses a mode decomposition approach to address the nonlinear nature of O3 concentration. This nonlinearity is one of the most challenging issues in air quality prediction, and this study proposed a model to decompose input data and identify the most relevant features and leverage attention mechanisms to produce weighted parameters that can enhance the model’s performance. The model’s performance enhancement approach was aimed at ensuring an effective model that easily adapts to frequently changing pollutant data in air quality prediction. The performance was evaluated statistically with root mean squared error (RMSE), mean absolute error (MAE), and mean square error (MSE). The proposed EEMD-CEEMDAN-BiLSTM-AMT model produced the most optimal result with MSE (4.80 × 10−6), RMSE (0.002), and MAE (0.001). When compared with the other similar models, the proposed model was best in terms of MSE value. Future work seeks to enhance the proposed model to fine-tune its performance on different air pollutant concentrations in South Africa.

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, as well as social behaviors and user habits, presents significant challenges. Traditional prediction models often struggle with capturing the complex nonlinear dynamics in multivariate time series, leading to low prediction accuracy. To address this issue, this paper proposes a new PV power prediction method that considers factors such as light, air pressure, wind direction, and social behavior, assigning different weights to them to accurately extract nonlinear feature relationships. The framework integrates long short-term memory (LSTM) and gated recurrent units (GRU) to capture local time features, while bidirectional LSTM (BiLSTM) and an attention mechanism extract global spatiotemporal relationships, effectively capturing key features related to historical output. This improves the accuracy of multi-step predictions. To verify the feasibility of the method for multivariate time series, we conducted experiments using PV power prediction as a scenario and compared the results with LSTM, CNN, BiLSTM, CNN-LSTM and GRU models. The experimental results show that the proposed method outperforms these models, with a mean absolute error (MAE) of 12.133, root mean square error (RMSE) of 14.234, mean absolute percentage error (MAPE) of 2.1%, and a coefficient of determination (R2) of 0.895. These results indicate the effectiveness and potential of the method in PV prediction tasks.

This paper presents a study on the development of a deep learning-based methodology for rapidly predicting the floor response spectrum (FRS) of multi-story buildings. The proposed approach aims to accurately predict both elastic and inelastic floor response spectra using surrogate-modelling techniques, significantly reducing the computational cost compared to traditional physics-based time-history analyses. Advanced deep learning techniques, such as bidirectional long short-term memory (Bi-LSTM) networks and Transformers are employed. These models are trained on datasets derived from numerical simulations as well as structural health monitoring data collected from real earthquake events. To develop and validate the models, numerical simulations conducted via OpenSees generate datasets comprising FRS data from 16 multi-story buildings modelled as simplified MDOF systems and 10 buildings modelled with detailed finite element representations. A total of 1030 ground motions are considered. In addition, FRS data from 96 multi-story buildings obtained from real earthquake events are incorporated into the model development process. The model’s performance is thoroughly evaluated using metrics, including root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), and the coefficient of determination (R2). The proposed surrogate model achieves an accuracy of 0.95, demonstrating its effectiveness in predicting the FRS of multi-story buildings.

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

In recent years, the process of industrialization and urbanization has accelerated the global economy, which benefits people but has serious environmental consequences. Air quality has become a major concern for both countries and individuals. We propose a very effective method for predicting PM2.5 levels in Chennai using Graph Convolutional Networks (GCN) and Bi-directional Long Short-term Memory (Bi-LSTM) models. As input features, we use historical PM2.5 data, wind speed, humidity, temperature and precipitation. To improve the model's robustness, data preprocessing methods such as Data Cleaning, Data, Data Transformation and Data Reduction was used.Randomly shifting the time-series data by a small time-interval, Randomly scaling the data by a small factor and Randomly applying noise to the data was done during data Augmentation. GCN works by propagating information between nodes in the graph, with each node's representation being updated based on the representations of its neighbors. In this case, the nodes would correspond to different locations in the city, and the edges would represent the spatial relationships between them. whereas the Bi-LSTM component captures the temporal relationships. The proposed method is evaluated on a real-world data-set and shows significant improvement in prediction accuracy compared to traditional methods. The performance of the model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE).

The developed research includes an Long Short-Term Memory (LSTM) network with IoT sensor data for the management of rice crop irrigation. Optimizing the amount of water required for rice crops is very critical, based on real-time predictions of the actual environmental and soil parameters including moisture, temperature, humidity, and rainfall as well as all other possible related factors. IoT sensors installed in agricultural fields gathered data, which was transmitted to a controller and then uploaded to the cloud for further processing. The training dataset contained 5,600 records. In this research, 70% was used for training and the rest 30% for testing. Three LSTM models were used for analyzing sensor data and predicting the need for irrigation and controlling the irrigation pump accordingly. These were Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM. It was evaluated the performance of the models developed using precision, recall, F1 score, accuracy, and other performance measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score. Bidirectional LSTM was determined to have the best performance, with MAE at 0.15, RMSE at 0.24, and an R2 score of 0.93, exhibiting high accuracy and predictive power. The Stacked LSTM and Vanilla LSTM also gave fair results, in terms of having R2 values of 0.89 and 0.84, respectively. The number of training and memory used for the computation were also given, with Vanilla LSTM taking least, followed by Stacked LSTM and Bidirectional LSTM. These results suggest that the LSTM networks can potentially be capable of improving the irrigation management such that more water is conserved and, subsequently, more suitable for agricultural productivity.

In the construction of ultra-high voltage (UHV) transformation substations, mass concrete is highly susceptible to temperature-induced cracking due to thermal gradients arising from the disparity between internal hydration heat and external environmental conditions. Such cracks can severely compromise the structural integrity and load-bearing capacity of foundations, making accurate temperature prediction and effective thermal control critical challenges in engineering practice. To address these challenges and enable real-time monitoring and dynamic regulation of temperature evolution, this study proposes a novel hybrid forecasting model named CPO-VMD-SSA-Transformer-GRU for predicting temperature behavior in mass concrete. First, sine wave simulations with varying sample sizes were conducted using three models: Transformer-GRU, VMD-Transformer-GRU, and CPO-VMD-SSA-Transformer-GRU. The results demonstrate that the proposed CPO-VMD-SSA-Transformer-GRU model achieves superior predictive accuracy and exhibits faster convergence toward theoretical values. Subsequently, four performance metrics were evaluated: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The model was then applied to predict temperature variations in mass concrete under laboratory conditions. For the univariate time series at Checkpoint 1, the evaluation metrics were MAE: 0.033736, MSE: 0.0018812, RMSE: 0.036127, and R2: 0.98832; at Checkpoint 2, the values were MAE: 0.016725, MSE: 0.00091304, RMSE: 0.019114, and R2: 0.96773. In addition, the proposed model was used to predict the temperature in the rising stage, indicating high reliability in capturing nonlinear and high-dimensional thermal dynamics in the whole construction process. Furthermore, the model was extended to multivariate time series to enhance its practical applicability in real-world concrete construction. At Checkpoint 1, the corresponding metrics were MAE: 0.56293, MSE: 0.34035, RMSE: 0.58339, and R2: 0.95414; at Checkpoint 2, they were MAE: 0.85052, MSE: 0.78779, RMSE: 0.88757, and R2: 0.91385. These results indicate significantly improved predictive performance compared to the univariate configuration, thereby further validating the accuracy, stability, and robustness of the multivariate CPO-VMD-SSA-Transformer-GRU framework. The model effectively captures complex temperature fluctuation patterns under dynamic environmental and operational conditions, enabling precise, reliable, and adaptive temperature forecasting. This comprehensive analysis establishes a robust methodological foundation for advanced temperature prediction and optimized thermal management strategies in real-world civil engineering applications.

Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential level is challenging due to the higher uncertainties involved. Recently deep neural networks have been leveraged to address this issue. This paper investigates the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based BiLSTM (CNN-BiLSTM) to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand. Using a publicly available dataset consisting of 34 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr. span, given the previous 24 hr. load data. The BiLSTM model achieved the lowest RMSE of 1.4842 for the overall daily forecast. In addition, standard LSTM and CNN-LSTM models are trained and compared with the BiLSTM architecture. The RMSE of BiLSTM is 5.60%, 2.85% and 2.60% lower than LSTM, CNN-LSTM and CNN-BiLSTM models respectively.

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

The accurate prediction of Municipal Solid Waste (MSW) electricity generation is very important for the fine management of a city. This paper selects Shanghai as the research object, through the construction of a Bidirectional Long Short-Term Memory (BiLSTM) model, and chooses six influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then, this study obtains the MSW electricity generation capacity in Shanghai by using the aforementioned prediction results and the calculation formula of theMSW electricity generation. The experimental results show that, firstly, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the BiLSTM model are 42.31, 7.390, and 63.32. Second, it is estimated that by 2025, the maximum and minimum production of MSW in Shanghai will be 17.35 million tons and 8.82 million tons under the three scenarios. Third, it is predicted that in 2025, the maximum and minimum electricity generation of Shanghai MSW under the three scenarios will be 512.752 GWh/y and 260.668 GWh/y. Finally, this paper can be used as a scientific information source for environmental sustainability decision-making for domestic MSW electricity generation technology.