The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things

Abstract

To promote the intelligent and efficient development of new energy grid connection management, this work first analyzes the current situation and problems in cost management for new energy grid connections. It is found that existing models are not effectively adaptable to complex and dynamic energy systems. Therefore, this work constructs a comprehensive monitoring system based on Internet of Things (IoT) technology. This system monitors and collects the energy production and consumption data in real-time to simulate the processes of new energy generation, storage, transmission, and consumption. The model considers different types of new energy resources, including solar, wind, and a time-series production simulation method is employed to simulate the energy production process. Finally, an improved Informer model for intelligent cost management for new energy grid connection is built. The research results indicate that with the penetration of new energy, the system’s idle capacity gradually increases, and the solar power generation also increases, but the utilization hours of solar energy slightly decrease. Moreover, the improved Informer model performs well in the management of new energy grid connections. The introduced Wasserstein distance improvement method positively enhances the model’s prediction accuracy, with a decrease of 208.4 in Mean Squared Error, a reduction of 145.6 in Root Mean Squared Error, and a decrease of 7.14 in Mean Absolute Error. This work provides an innovative solution for IoT-based cost management of new energy grid connections, having theoretical significance and practical value.

Keywords

• Time-series production simulation
• deep learning
• Internet of Things
• new energy grid connection
• cost management