[Objective] Traditional ventilation approaches for underground powerhouses lack adaptability to changing environmental conditions, leading to insufficient pollutant removal efficiency when concentrations are high, and excessive energy consumption when demand is low. This study proposes an intelligent energy-saving control method for underground powerhouse ventilation by integrating a highly accurate dynamic demand prediction model with a robust fan control strategy, seeking to ensure environmental safety compliance while minimizing energy waste during the construction phase. [Methods] The intelligent control framework comprises three main components:variable selection, predictive modeling, and control execution.(1) Input Parameter Selection: Utilizing Mutual Information (MI) analysis on 41 sets of field data, the study identified CO concentration, dust concentration, and ventilation time as the key input variables for the demand prediction model, effectively filtering out redundant environmental factors to enhance model efficiency.(2) IPSO-RBF Prediction Model: A Radial Basis Function (RBF) neural network was constructed to predict the required wind speed in real-time. An Improved Particle Swarm Optimization (IPSO) algorithm was introduced. The IPSO algorithm employs non-linear adjustment strategies for inertia weight (ω) and learning factors (c₁, c₂). (3) Fuzzy PID Control Strategy: Recognizing the large inertia and hysteresis of ventilation systems, a Fuzzy PID (Proportion Integration Differentiation) control algorithm was developed to replace traditional PID control. This method utilizes fuzzy logic rules to self-tune K_p, K_i, and K_d based on system error (e) and error change rate (e_c), ensuring rapid and stable fan frequency adjustment.(4) System Integration: The proposed methods were integrated into a Web-based intelligent energy-saving feedback control platform. The system was deployed at the Lushan Pumped Storage Power Station in Henan Province, utilizing a custom-built mobile integrated sensor box and a Siemens PLC-based control layer for field validation. [Results] The performance of the proposed method was validated through numerical experiments and on-site engineering applications.(1) Prediction Accuracy: The IPSO-RBF model significantly outperformed both the traditional RBF and standard PSO-RBF models. Specifically, on the test set, the IPSO-RBF model achieved a Mean Square Error (MSE) of 0.25, a Mean Absolute Error (MAE) of 0.38, and a Mean Absolute Percentage Error (MAPE) of 2.37%. The relative error of the proposed model was consistently kept within ±5%, demonstrating superior precision in predicting ventilation demand under complex working conditions.(2) Control Performance: Fuzzy PID algorithm significantly improved the dynamic response of the fan frequency control. Compared to traditional PID algorithm, the Fuzzy PID method reduced the system overshoot by 7% and shortened the settling time (time to reach stability) by 37.5% (from 45 s to 30 s), proving its strong robustness against system lag.(3) Field Application: Field tests following blasting operations at the Lushan project confirmed the system’s effectiveness. The platform successfully reduced CO and dust concentrations to regulated safety standards within the required timeframe (30 min). In terms of energy efficiency, the intelligent control mode consumed 121.9 kW·h of electricity during the test period, whereas the traditional “one wind blowing” mode consumed 154.7 kW·h, a significant energy saving of 21.2% per ventilation cycle. [Conclusion] The proposed IPSO-RBF model provides highly accurate, real-time predictions of ventilation demand by effectively handling small-sample data in complex environments. Fuzzy PID strategy further solves the control challenges associated with the large inertia of ventilation systems, offering faster response times and greater stability than conventional methods. The deployment of the Web-based intelligent platform at the Lushan Pumped Storage Power Station verifies the practical engineering value of this approach. By achieving a 21.2% reduction in energy consumption while guaranteeing air quality compliance, the model provides a generalized, efficient, and intelligent solution for ventilation management in the construction of large-scale underground hydropower facilities, contributing to the industry’s goals of smart construction and green development.