The Modbus protocol serves as a fundamental element in modern computer network systems, with Modbus Transmission Control Protocol (TCP) being particularly vital in the realms of edge computing and industrial computing. Although User Datagram Protocol (UDP) is frequently acknowledged for its superior transmission speed relative to TCP, it is deficient in the reliability that TCP offers. Modbus utilizes the attributes of TCP to ensure accurate data transmission; however, it exhibits inherent limitations when managing large data volumes, which negatively impacts the performance of the communication link. To address this challenge, we propose an innovative approach referred to as Modbus UDP over Time-Sensitive Networking (TSN). This method not only significantly improves transmission performance, but also leverages the benefits of TSN to rectify the reliability shortcomings associated with UDP. Experimental results obtained from the testing platform indicate that this approach can markedly enhance the capacity for lossless data transmission.

Accurate Photovoltaic (PV) generation forecasts can reduce power redeploy from the grid, thus increasing the supplier’s profit in the day-ahead electricity market. However, the PV process is affected differently by various factors under different weather conditions, resulting in significantly different energy output curves. In this context, this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism. We propose a Multi-Stream Attention Fusion Network (MSAFN) which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions. The proposed Conditional Decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions. Notably, the attention modules adeptly learn patterns under diverse conditions, while simultaneously, the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time. We compare the state-of-the-art decomposition algorithms (VMD, EEMD, MSTL, etc.) and prediction models (BPN, LSTM, XGBoost, transformer, etc.) commonly used in PV prediction. The results show that the MSAFN model is more accurate than the models above, which has a noticeable improvement compared to other recent day-ahead PV predictions on Desert Knowledge Australia Solar Centre (DKASC) dataset.