Due to the prevalent “small data”, “seasonal”, and “periodicity” characteristics in China’s renewable energy power generation data, there are certain difficulties in long-term power generation prediction. For this reason, this paper uses the data preprocessing method of periodical aggregation to enhance the “quasi-exponentiality” characteristics of original data, eliminate “seasonality” and “periodicity”, use the DGM (1,1) model to predict aggregated data, and then use the periodical component factor to reduce the DGM (1,1)-predicted data. A seasonal discrete grey prediction model based on periodical aggregation is constructed. The proposed methodology employs streamlined data preprocessing coupled with conventional grey prediction modeling to enable the precise forecasting of nonlinear periodic sequences. This approach demonstrates an enhanced operational efficiency by mitigating the structural complexity and implementation barriers inherent in classical seasonal grey prediction frameworks. Validation experiments conducted on China’s photovoltaic (PV) and wind power generation datasets through comparative multi-model analysis confirm the model’s superior predictive accuracy, with performance metrics significantly outperforming benchmark methods across both training and validation cohorts.

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(4⁴) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to complete icing of asphalt pavements, achieving a prediction accuracy (PA) of 90.7% for the time to complete icing and a mean error of merely 0.71 min. Dynamic icing risk thresholds (high/medium/low) were established via K-means clustering and statistical tests, enabling data-driven precise activation and on-demand regulation of geothermal deicing systems. This resolves energy waste and deicing delays, offering technical support for efficient geothermal utilization in cold-region transportation infrastructure, and provides a scalable “factor screening + model prediction” framework for asphalt pavement anti-icing practice.

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

To address high thermal loads and energy costs in Geothermal Road Snow-Melting Systems (GRSSs) within cold regions, this study optimizes energy efficiency through material-level intervention. We developed a composite anti-icing modifier synergistic with low enthalpy geothermal systems, comprising slow-release agents, anti-corrosive components, reinforcing materials, and active chloride salts. By regulating the thermodynamic boundary of the pavement, the freezing point is suppressed to −21 °C. This eliminates the requirement for positive pavement temperatures, significantly reducing the design thermal power. Chloride ion release patterns were analyzed via dissolution and 20-day soaking tests to evaluate structural durability. Results show optimal performance at a 5% modifier dosage and 5.3% bitumen aggregate ratio. Ion release follows a third-order polynomial law and remains stable at 35 °C, ensuring reliability during seasonal thermal cycles. Validation in Xinjiang showed a variation of only 1.5% over 20 days. This research offers an innovative material energy synergy for cascaded geothermal utilization and infrastructure decarbonization in cold regions.