Data-Driven Identification of Operating Thresholds for Cycling Reduction in Chiller Systems

Chiller systems account for a substantial proportion of building energy consumption, where their operational efficiency and start–stop cycling frequency directly influence overall system energy use and equipment lifespan. In practical applications, load fluctuations and improper control settings often cause chillers to experience frequent cycling, leading to decreased efficiency and increased mechanical wear. While existing studies predominantly focus on real-time control or model predictive approaches, fewer investigations systematically identify stable operating regions and optimal control thresholds using historical operational data. This study proposes a data-driven method for identifying an operational threshold. Long-term historical data are analyzed to establish a start–stop event detection mechanism. A normalized power index is introduced, and multi-scenario classification—incorporating seasonal conditions and peak/off-peak periods—is employed to evaluate system behavior across different contexts. Furthermore, a quantile scanning approach combined with hysteresis simulation is utilized to identify optimal operational threshold intervals. Stability evaluation indices, based on cycling frequency, power variation rate, and load deviation magnitude, are constructed to quantify stability performance. To verify the robustness of these thresholds, K-fold cross-validation is performed. Results indicate that the identified thresholds effectively reduce cycling frequency and power fluctuations, thereby enhancing system stability. Specifically, the start–stop cycling frequency is reduced by approximately 75–90%, and the power variation rate decreases by up to 85% across various scenarios. This study provides an offline decision-support framework to assist operators in optimizing control parameters and strategies. These outcomes serve as a reference for chiller energy management and provide empirical evidence for the future design of control strategies.