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Solar PV systems often face challenges feeding power into the local grid due to weather dependence. Although solar PV generation is variable, it is predictable and can help maintain grid stability. In this study, a modified neural network is developed to forecast power generation for a 500-kW solar farm under Thailand’s climatic conditions. Year-round operational data from the solar PV plant are used to train the forecasting model, and over 15% of the period is reserved for power generation prediction and validation against the actual power profile. Keras provides an effective interface, with TensorFlow as the backend engine, which is well-suited for high-computation processes. For training and testing, a batch size of 32 and 50 epochs is used as standard parameters, helping avoid overfitting and improving computational efficiency. It was found that during 75% of the sunshine period, the solar PV system generated 50% of its nominal DC capacity, indicating efficient operation. A 0.22 kW average difference between the forecasting model and the actual power profile indicates 99.86% accuracy over the testing period. The difference between actual and predicted power ranged from 2.88 kW to -4.67 kW, and the corresponding MAE, MSE, and RMSE were 0.87, 1.32, and 1.15, respectively. Furthermore, the developed ANN-based forecasting model is highly recommended for commercial use to avoid penalties from the grid authority and enhance grid stability.

The Quadric Error Metrics (QEM) algorithm is widely used for 3D mesh simplification, but it struggles with models containing discontinuous appearance attributes and often fails to preserve fine local details. To address these issues, we propose a textured mesh simplification method that incorporates multiple feature constraints. The approach introduces tailored edge collapse rules and a new error metric, the seam angle error, to handle discontinuous texture regions. In addition, vertex sharpness and texture complexity are employed to enhance the algorithm’s sensitivity to geometric and textural details. Experimental results demonstrate that the proposed method effectively reduces texture distortion at seams. Specifically, at an 80% simplification rate, our method reduces the texture root mean square (RMS) error by approximately 78% compared to T-QEM. While this approach incurs a slight increase in global geometric error, it significantly preserves local visual details and texture continuity.

In this article, the error metrics used for evaluating the performance of direction of arrival (DoA) estimation are thoroughly investigated to recommend the most suitable one. This investigation highlights the lack of consensus in the literature regarding the selection and definition of these metrics. We show that this disparity is particularly serious in 2D DoA estimation, an aspect often overlooked by many authors. Notably, certain widely accepted error metrics can yield inaccurate and misleading results. Therefore, this article advocates for the adoption of a specific error metric that ensures accurate and meaningful assessments of 2D DoA estimation. A set of numerical and experimental results is presented to demonstrate the potential of the proposed error metric compared to other well-known metrics. Unlike other metrics, our proposed error definition is frame-independent. Finally, practical use cases are briefly discussed to highlight the pervasive impact of this fundamental definition.

Transport of pollutants is a serious environmental concern, where accurate and effective mathematical models are essential for developing viable mitigation programs. In this work, this study proposes new formulation of advection dispersion equations of fractional order and employ them to model the highly complex advection dispersion phenomena. The derivative using the Modified Atangana–Baleanu–Caputo (MABC) fractional derivative is an advanced extension of the classical Atangana Baleanu derivative and provides greater flexibility in describing memory and nonlocal effects. To solve the resulting problem numerically, we utilize the framework of physics informed neural networks (PINNs), in which the governing physical laws serve as the building blocks of a deep learning model. This approach enables the derivation of highly accurate and fast convergent semi-analytical solutions. The main contributions of this work are threefold: (1) the development of specific PINNs algorithm to solve fractional differential equations in the MABC sense; (2) an extensive performance analysis demonstrating higher precision and computational efficiency compared to conventional numerical and perturbative methods; and (3) validation through a variety of case studies, confirming the robustness and applicability of the proposed approach in different contexts. Several numerical examples are provided to illustrate the effectiveness of the approach, and the results are compared with existing methods to justify both the efficiency and feasibility of the proposed scheme.

Lightweighting Building Information Modeling (BIM) models for digital-twin applications requires balancing aggressive geometric reduction with component-level engineering tolerances and mesh usability. Most geometric simplification pipelines apply uniform ratios or hand-tuned heuristics, which struggle to accommodate the strong heterogeneity of BIM components in functional role, geometric complexity, and detail distribution. End-to-end learning-based simplification can be adaptive, but it often entangles decision-making with geometric editing, making engineering constraints difficult to enforce and audit. We present Semantic-Geometric Co-driven Adaptive Budget Estimation and Reduction for BIM (SABER-BIM), which formulates lightweighting as a component-level face-budget allocation problem. Conditioned on Industry Foundation Classes (IFC) types and structure-sensitive geometric descriptors, SABER-BIM predicts target face counts for individual components and then meets a user-specified global budget through global scaling. The predicted budgets are executed by a robust geometric backend (e.g., quadric error metrics, QEM), yielding an auditable and easily deployable pipeline. To address the absence of direct supervision, we introduce an offline pseudo-ground-truth procedure that searches for the minimum feasible target face count for each component under semantic-aware tolerance and mesh-validity constraints. Experiments on the IFCNet dataset show that SABER-BIM allocates budgets more effectively under identical global constraints, improving stability in both geometric error control and engineering usability.

The rapid rise in building construction creates the energy demand for nearly half of the world's energy demand. To minimize energy consumption in buildings, a concept called net-zero energy building (NZEB) is gaining popularity in developing countries and is being implemented in India as well. The NZEB aims to match the on-site renewable energy generation available at the building location with the building energy consumption (BEC) without relying on grid energy. To attain this concept in a real-time scenario, it requires information about the energy generation at the building and the energy consumption at each instant. It is necessary to predict the dynamically varying building loads to easily manage the available sources without the involvement of the grid. This can be achieved by designing an accurate prediction model. This article presents a comparative assessment of recent metaheuristic algorithms for hyperparameter optimization of a support vector regression (SVR) model to enhance the prediction performance of BEC. The analysis was conducted using hourly campus-scale energy consumption data collected from the National Institute of Technology Silchar, Assam, India, from 1 March 2018 to 29 February 2020, comprising 17,544 samples. The Polar Fox Optimization algorithm, Flood Algorithm, and Hiking Optimization Algorithm (HOA) were comparatively evaluated for SVR hyperparameter tuning in this application. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), R 2 , percentage BIAS (PBIAS), and Willmott's Index (WI) error metrics are used to evaluate the performance of the optimized SVR models. The recently developed HOA algorithm exhibits better prediction accuracy with an MAE of 8.3099 kWh, RMSE of 11.1283 kWh, R 2 of 0.9986, MAPE of 2.7820%, PBIAS of −0.0759%, and WI of 0.9996 when compared to other models. The comparative results for different models show that the recent metaheuristic optimization methods can improve the performance of SVR model for accurate BEC prediction in NZEB applications.

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested.

This paper presents a rigorous and critical methodology for evaluating the performance of deep learning (DL) techniques in predicting mechanical responses within the microstructural representation of composites. In the past few years, deep learning has emerged as a powerful tool and an efficient surrogate for finite element analysis in computational mechanics. This research addresses questions regarding the suitability of common error metrics for evaluating the accuracy of DL techniques in predicting full-field mechanical responses of composites. Through comparative analysis, we evaluate the performance and identify the limitations of two DL frameworks in predicting the linear von Mises stress distribution within the microstructure of the selected fiber-reinforced composite. The first DL method is based on the residual network, while the second utilizes U-Net architecture. We use several evaluation metrics, including different types of error and statistical measures and examine their suitability. Additionally, this study also investigates the influence of the size of the training and validation dataset, ranging from 50 to 2000 samples, on the predictive performance of the employed ResNet and U-Net based approaches.

Potatoes are one of the widely consumed staple foods all over the world. The prices of potatoes were more unstable than other agricultural commodities because of factors such as perishability, production uncertainties, and seasonal fluctuations. These factors make it difficult for farmers to manage and predict production levels, resulting in supply and price fluctuations. Therefore, it is essential to develop predictive models that can accurately forecast the pricing of agricultural commodities such as potatoes. The study attempted to explore the pattern of potato prices in major markets of northern India using different time series models. The empirical findings indicated positively skewed data distributed with a high instability index. In terms of forecasting accuracy, the EEMD-ANN model exhibited the best performance among the various time series techniques, generating the lowest MAPE values of 9.10%, 12.97%, and 4.27% for the Chandigarh, Delhi, and Shimla markets, respectively. Meanwhile, the EEMD-ARIMA model yielded the most accurate prediction results for the Dehradun market, with an MAPE value of 12.97%. The outcomes of this study offer significant insights to farmers, consumers, and government bodies for making informed decisions regarding the production, consumption, and distribution of potatoes. Moreover, the effectiveness of various time series models in handling complex agricultural price series was also investigated.