Orthogonal Time Frequency Space (OTFS) modulation has been verified in high-mobility scenarios as an effective solution to address the Doppler shift effect. To further improve the data transmission efficiency of the system, this paper delves into different channel coding techniques in the Delay-Doppler (DD) domain under OTFS modulation. This paper evaluates the system performance of various encoding schemes under multiple user mobility rates, including Convolutional codes, Turbo codes, Low-Density Parity-Check (LDPC) codes, and Polar codes. Additionally, this paper investigates the impact of adopting different modulation constellation mapping schemes on the system’s Bit Error Rate (BER) and explores strategies for enhancing system performance in high-speed data transmission scenarios. The simulation results show that the code-based system reduces the BER by about 17-35% compared to the uncoded OTFS system. In this case, the LDPC code system has a 10 dB Signal-to-Noise Ratio (SNR) gain. The simulation results demonstrate that combining coding techniques with OTFS modulation can significantly enhance the performance of communication systems in highly dynamic environments, with LDPC and Turbo codes showing notable advantages in performance improvement. The findings of this paper not only highlight the importance of choosing the right coding scheme and provide valuable references for the design of high-speed mobile communication systems in the DD domain.

This research introduces a model with two distinct stages for accurate forecasting and efficient energy sharing within grid-integrated community networks (GICNs), which combines residential power loads, generators of wind and photovoltaic (PV) power, and energy storage (ES). In the first-stage system model, we employ a machine learning (ML) algorithm for day-ahead supply and demand forecasting to improve the forecasting accuracy. Specifically, we develop a hybrid convolutional neural network with long short-term memory (CNN-LSTM), which effectively includes both spatial and temporal dimensions of time series data. In the second stage, a bidirectional real-time energy sharing strategy is designed based on forecasted data, facilitating the efficient distribution of surplus energy among communities. The two-stage system model integrates forecasting and energy sharing to accurately predict supply and demand, as well as effective energy sharing among GICN participants. The measurements including mean absolute error (MAE), root mean squared error (RMSE), and generation utilization rate are defined to evaluate prediction accuracy and energy sharing effectiveness thereby ensuring optimal grid operation and sustainability. Finally, the proposed hybrid CNN-LSTM algorithm is compared with the single LSTM and CNN models to demonstrate the performance superiority of the hybrid CNN-LSTM model. Numerical simulation experiments indicate that the proposed model CNN-LSTM achieved high accuracy in predicting user PV, wind, and load demand. Additionally, the implemented real-time energy sharing strategy efficiently manages energy distribution within GICNs.

In dimensionality reduction techniques an important step is to construct optimal similarity graph to achieve effective classification results. The graph construction process in many existing algorithms is manual and thus severely affects the classification performance, if the neighborhood parameter is not optimal. Moreover, existing methods that are based on Collaborative representation lack the between-class information in the embedding process. In this paper, we addressed the problem of automatic Graph construction which is datum adaptive and incorporates within-class and between-class information into the linear representation to learn optimal projection for dimensionality reduction using the Collaborative representation technique. To optimize graph construction, the proposed method used the L₂ norm graph and log-Euclidean distance. The resultant graph shows local properties by Collaborative representation and global discriminate information is represented by a Maximum Margin classifier (MMC). The MMC maximizes “between-class scatter” and minimizes “within-class scatter”, without locality information. Further for effective and accurate performance for image classification real databases will be incorporated. The experimental results have demonstrated that the proposed methods achieved competitive results with compared methods.

Decentralized energy systems (DESs) present a paradigm shift toward more sustainable and resilient electricity networks with the increasing integration of renewable energy sources. Accurately forecasting energy supply and electricity demand is crucial for the efficient operation of DES. This paper focuses on accurate short-term energy supply and demand forecasting using machine learning (ML) algorithms across different seasons within small-scale DES, including photovoltaic (PV) generation, wind generation, and load demand. Initially, we develop multiple base model ML algorithms including long short-term memory (LSTM), convolutional neural networks (CNN), eXtreme gradient boosting (XGBoost), and recurrent neural networks (RNN) with feature selection approaches to improve forecasting accuracy and reduce model complexity. These base models leverage key temporal and spatial features and seasonal variations process to improve the model forecasting accuracy and reduce overfitting across different seasons. To further reduce forecast errors, we propose robust ensemble forecasting techniques including simple averaging (SA), weighted averaging (WA), and Stacking. In ML algorithms, the ensemble forecasting technique combines the multiple base models’ forecasts to produce a more accurate and robust final forecast by leveraging the strengths and compensating for the weaknesses of individual base models. Finally, numerical simulations are conducted using Python, Keras, and TensorFlow libraries to develop, train, evaluate, and validate the effectiveness and accuracy of the developed forecasting models and the proposed ensemble forecasting techniques. The results demonstrate that the proposed approach offers a robust solution for short-term supply and demand forecasting problems in DES. The work is both novel and effective from the perspectives of application, ML algorithms combination, and performance improvement.