Accurate forecasting of renewable energy generation is critical for sustainable energy planning in rapidly urbanizing cities like Dhaka. This study conducts a comprehensive comparative analysis of classical time-series models ARIMA and SARIMA and a deep learning model LSTM for long-term combined renewable energy (CED) forecasting. Feature engineering was employed to enhance model inputs and both statistical and recursive deep learning approaches were utilized to predict combined renewable energy (CED) density over a two-year horizon (2024–2025). The analysis used 10 years of hourly wind and solar energy data (2014–2023), comprising 87,648 data points, sourced from the NASA POWER database. The ARIMA(2,1,2) and SARIMA(2,1,2)(1,1,1,24) models captured basic temporal patterns but struggled with non-linear dynamics, achieving R² scores of − 0.0008 and − 0.1104, respectively. In contrast, the LSTM model achieved superior performance (R² = 0.9860) using a fixed test set and demonstrated strong generalizability under time-series cross-validation (Avg R² = 0.9847) by effectively learning complex temporal dependencies. The LSTM-based long-term forecasts demonstrated realistic seasonal trends and robust temporal consistency. These findings underscore the transformative potential of deep learning in enhancing renewable energy forecasting accuracy in developing urban regions, providing critical insights for future energy policy and infrastructure development in Dhaka City.

This study evaluated the performance of multiple models that used machine learning to anticipate wind speed in the city of Dhaka. The NASA Power website provided the data set for this investigation. The models used for prediction included the decision tree regressor, support vector regressor, random forest, linear regression, neural network and polynomial regression. A hold-out check and k-fold cross-validation were used to assess how well these models performed. With the highest R2 scores and lowest RMSEs on both the validation and test sets, the results demonstrated that the polynomial regression model performed the best. With the lowest R2 scores and largest RMSEs on both sets, the decision tree model scored the poorest. High R2 scores and low RMSEs were achieved by the random forest model, which had comparable performance to the polynomial regression model but required a longer computation time. In addition, the neural network model demonstrated commendable predictive accuracy, yielding an R2 score of 0.67 and a low RMSE of 0.57. However, its application is contingent on the availability of substantial computational resources, given its extensive computation time of 457.93 s. The study concludes by highlighting the efficacy of the Polynomial Regression model as the optimal choice for wind speed prediction in Dhaka, offering a balance between superior performance and efficient computation. This insight provides valuable guidance for practitioners and researchers seeking effective models for similar applications.

This study gives a thorough analysis on the wind energy potential in Dhaka, Bangladesh, utilizing data from NASA Power's remote sensing tools and weather data from the Bangladesh Meteorological Department (BMD). The wind speed data collected over a 22 year period at an altitude of 10 m. The results indicate that 3.07 m per second (ms⁻¹) is Dhaka city's typical wind speed, while the maximum wind speeds were recorded in June and July. A Weibull distribution is used to observe the wind data, as well as to calculate the Weibull form parameter of 2.65 and the scale parameter of 3.43 ms⁻¹. Based on these parameters, the most probable wind speed along with the wind speed carrying maximum energy were calculated 2.83 ms⁻¹ and 4.28 ms⁻¹, respectively. The highest density of energy has been found in the month of July with a value of 52.11 W/m². According to the study, the south is the most prominent wind direction for Dhaka city. Moreover, the study analyzes the relations between energy density and other variables, like wind speed, humidity, dry bulb temperature, etc. Positive correlations between energy density, wind speed, and dry bulb temperature imply that the higher wind speeds and dry bulb temperatures result in greater energies. The study's conclusions offer intuitive information about Dhaka City's potential for wind energy and can support direct future efforts to pursue this green resource in alignment with the Sustainable Development Goals (SDGs) of Bangladesh.

This study proposes a polynomial regression model with Ridge regularization for forecasting the combined renewable energy generation (solar and wind) in Dhaka City. The model was developed using over a decade of meteorological data (2014–2023), capturing non-linear interactions among key predictors such as temperature, wind speed, and solar irradiance. Data preprocessing included normalization and outlier handling. Model performance was evaluated using standard metrics—mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²)—with 80/20 train-test splitting and 05-fold cross-validation ensuring generalizability. The Ridge-regularized model achieved an R² of 0.99 on the test data, with very low normalized error values (MSE ≈ 2.42 × 10⁻⁸, MAE ≈ 7.59 × 10⁻⁵), corresponding to an RMSE of approximately 1.55 × 10⁻⁴. While these error values are small due to normalization, the R² score provides a robust, scale-independent measure of performance. The high accuracy is supported by the strong correlations among features and the model’s ability to generalize effectively across cross-validation folds. By integrating wind and solar forecasting under a unified framework and applying Ridge regularization to manage multicollinearity, this work offers a reliable and interpretable approach for renewable energy planning in rapidly urbanizing regions.

Over the last few years for sensing applications in terahertz regime photonic crystal fiber (PCF) has gained attention quite extensively. The optical characteristics of photonic crystal fiber can be controlled by fine-tuning of the structural parameters like core radius and effecctive area. In this context, a terahertz sensor based on a hollow-core photonic crystal fiber has been developed for chemical identification in the terahertz frequency range with very low loss. The proposed structure contains hexagonal manner sectored suspension type cladding and hexagonal manner sectored core region, all the sector are formed by zeonex based struts. To investigate the optical characteristics of developed design, finite element method (FEM) based COMSOL multiphysics v.5.4a software has been used. The simulation result shows the sensitivity of 83.37% and 83.63% at the optimum condition in x-polarization mode for ethanol and benzene respectively with low effective material loss of 0.0291 cm−1 and low confinement loss of 1.87×10-13 cm-1. Moreover, the developed design implementation is possible in the existing fabrication method. Physical features and comparative performance analysis are also showed in this research. DUJASE Vol. 7 (2) 56-61, 2022 (July)