In order to address the challenge of achieving optimal prediction accuracy when a single prediction model faced with changes in meteorological conditions of different weather types, this paper proposes a photovoltaic (PV) power prediction method based on the combination of similar day clustering and convolutional neural network (CNN)-gated recurrent unit (GRU). The Pearson correlation coefficient and Spearman’s correlation coefficient are used to filter out the key features such as total solar radiation and module temperature to construct a new input dataset; the K-means algorithm is used to perform clustering analysis on the data, and the data are classified into sunny, cloudy, and rainy days; the spatial correlation features of the meteorological factors are extracted by using the convolutional neural network (CNN), and the CNN-GRU model is established by combining with the gated recurrent units (GRUs). The PV output power is predicted based on the PV power data and the corresponding meteorological data from a place in Ningxia, collected during June to August 2020, and the method proposed in the article is tested. Validation results show that, compared to other models, the model proposed in this paper reduces MAE and RMSE by 66.1% and 65.7% on average under three different weather type scenarios, and improves R² by 19.8% on average. This verifies that the model has high prediction accuracy and generalization ability, achieving better results in PV output power prediction. The CNN-GRU model demonstrates superior capability in modeling short- and long-term dependencies compared to other deep learning hybrid approaches, while also achieving higher computational efficiency and faster training convergence.

As the adoption of electric vehicles (EVs) continues to rise, the pressure on charging station resources has intensified, particularly under high-load conditions, where limited charging infrastructure struggles to meet the growing demand. Issues such as uneven resource allocation, prolonged charging wait times, fairness concerns among different user groups, and inefficient scheduling strategies have significantly impacted the overall operational efficiency of charging infrastructure and the user experience. Against this backdrop, the effective management of charging infrastructure has become increasingly critical, especially in balancing the diverse mobility needs and service expectations of users. Traditional charging scheduling methods often rely on static or rule-based strategies, which lack the flexibility to adapt to dynamic load environments. This rigidity hinders optimal resource allocation, leading to low charging pile utilization and reduced charging efficiency for users. To address this, we propose an Adaptive Charging Priority (ACP) strategy aimed at enhancing charging resource utilization and improving user experience. The key innovations include (1) dynamic adjustment of priority parameters for optimized resource allocation; (2) a dynamic charging station reservation algorithm based on load status and user arrival rates to prioritize high-priority users; (3) a scheduling strategy for low-priority vehicles to minimize waiting times for non-reserved vehicles; and (4) integration of real-time data with the DDPDQN algorithm for dynamic resource allocation and user matching. Simulation results indicate that the ACP strategy outperforms the FIFS and RFWDA strategies under high-load conditions (High-priority vehicle arrival rate: 22 EV/h, random vehicle arrival rate: 13 EV/h, maximum parking duration: 1200 s). Specifically, the ACP strategy reduces charging wait times by 96 s and 28 s, respectively, and charging journey times by 452 s and 73 s. Additionally, charging station utilization increases by 19.5% and 11.3%. For reserved vehicles, the ACP strategy reduces waiting times and journey times by 27 s and 188 s, respectively, while increasing the number of fully charged vehicles by 104. For non-reserved vehicles, waiting and journey times decrease by 213 s and 218 s, respectively, with a 75 s increase in fully charged vehicles. Overall, the ACP strategy outperforms traditional methods across several key metrics, demonstrating its advantages in resource optimization and scheduling.

The primary pollutants emitted from mobile sources have become one of the main sources of urban air pollution. In this study, the primary pollutants CO, HC, NOx and PM from mobile sources in Zibo city, China are studied. Firstly, the localized mobile-source pollutant emission factors are corrected using vehicle emission experiments; secondly, multi-source data fusion is performed using road network data, road traffic data, air quality and meteorological data; then, the corrected pollutant emission factors and multi-source data are used to construct a localized emission measurement model and dispersion simulation model, visualize the emission distribution and propose residual concentration correction to accurately obtain the pollutant sharing rate. The results show that the pollutant emission trends are highly coupled with the distribution of urban residents’ working hours. Based on the localized dispersion model used to analyze the pollutant traceability at air monitoring stations, the emission sharing rate of NOx is the largest, and according to the analysis of the changing trend of mobile-source emission sharing rate, the mobile source pollutants in Zibo are mainly influenced by pollutant emissions from road motor vehicles. This study provides data support and a theoretical basis for the management of the transportation industry to carry out exhaust gas control of motor vehicles and non-road mobile machinery and to make decisions related to improving the air environment (delineating the scope of low emission zones).