Application of TCN Edge Operation Model in Photovoltaic Power Generation Forecasting

With the rapid development of edge computing technology, efficiently deploying AI models on resource-constrained devices becomes an important challenge. Edge computing requires devices to perform efficient inference under limited computational and storage resources to meet the demands of real-time applications. Temporal Convolutional Networks (TCN) demonstrate excellent parallel computing capabilities and the ability to model long-term dependencies, making them particularly effective in time-series prediction tasks, such as photovoltaic (PV) power generation forecasting. PV power generation influences factors such as weather, sunlight intensity, and temperature. Accurate prediction of PV power generation is crucial for energy management, optimizing grid scheduling, and improving energy utilization. This study explores how to deploy the TCN model on the Rockchip RK3588 edge device and apply it to PV power generation forecasting. This paper provides a practical case for the application of AI models on resource-constrained devices, verifies the feasibility of deploying TCN in edge computing environments, and offers practical experience and theoretical support for the optimization and application of AI models on future edge devices.