Data-driven machine learning model estimates efficiency gains from passive filters under variable loads

• A two-step ML model predicts passive filter efficiency using field measurements. • Leverages real-world data, overcoming simulation limits for site-specific estimates. • Selected top features by Ridge Regression used by XGBoost to improve prediction. • Neutral current, unbalance, harmonics, power factor: key drivers of efficiency gains. • Enables data-driven investment for confident passive filter deployment. Accurately estimating power loss reduction from passive filters before installation is challenging due to variable loads and power quality conditions across grid points. Existing studies rely on simulation or analytical models. These approaches often fail to capture real-world variability through data-driven methods. This gap limits effective, site-specific filter deployment decisions. We present a two-step machine learning approach to estimate energy efficiency gains from passive filters under variable conditions using high-resolution power analyzer data. Ridge Regression identifies key predictive variables, achieving baseline R² = 0.591. XGBoost then captures nonlinear interactions between load variability, power quality disturbances, and filter performance, improving accuracy to R² = 0.755. The methodology was validated through deployment at three industrial facilities in collaboration with Livarsa GmbH. Results demonstrate 9.9% average relative error across measured efficiency gains, confirming reliability under real-world conditions. Comprehensive validation through k-fold cross-validation, ensemble methods, and external testing quantified prediction uncertainty inherent in small industrial datasets (25 training samples). The approach offers a scalable, data-driven decision-support tool overcoming simulation-based limitations. Computational efficiency enables real-time assessment during client consultations without specialized software. Economic value derives from reduced performance guarantee margins, accelerated assessment timelines, and minimized warranty exposure. Limitations include statistical constraints from limited training data, reflected in cross-validation overfitting and wide confidence intervals. External validity requires site-specific validation for facilities with substantially different electrical characteristics. Despite these constraints, the findings provide practical value for energy professionals seeking efficient power quality solutions, enabling confident passive filter deployment decisions based on quantified performance predictions.