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Go to Editorial ManagerThe electrical load is affected by the weather conditions in many countries as well as in Iraq. The weather-sensitive electrical load is, usually, divided into two components, a weather-sensitive component, and a weather-insensitive component. The research provides a method for separating the weather-sensitive electrical load into five components. and aims to prove the efficiency of the five-component load Forecasting model. The artificial neural network was used to predict the weather-sensitive electrical load using the MATLAB R17a software. Weather data and loads were used for one year for Mosul City. The performance of the artificial neural network was evaluated using the mean squared error and the mean absolute percentage error. The results indicate the accuracy of the prediction model used, MAPE equal to 0.0402.
This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities.
In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.