Physics-guided machine learning framework for performance and economic assessment of a pico-hydroelectric system

• A fully integrated framework combining experimental testing, physics-guided machine learning, and techno-economic assessment was developed for a locally manufactured 3 kW Pelton-type pico-hydropower system. • Physics-guided Ridge Regression achieved high predictive accuracy (RMSE = 0.045 kW, R² = 0.992) and showed narrow uncertainty bounds through bootstrap analysis, confirming robustness under limited data. • Forward power maps and inverse head–flow envelopes were generated, enabling design-point estimation and performance extrapolation up to the 100 m operating head with only 1.1 % deviation from analytical hydropower predictions. • Comparative evaluation demonstrated that the KRW-3000 achieves 60.5 % efficiency, outperforming previously reported low-head prototypes (20–40 %) and matching commercial Pelton turbines. • Techno-economic assessment shows strong financial competitiveness, yielding LCOE = 0.0075 USD/kWh, payback < 1 year versus diesel generation, and payback = 4.2 years versus grid electricity. Pico-hydropower offers an effective solution for high-head, low-flow rural electrification; however, most existing studies examine hydraulic performance, machine-learning prediction, and techno-economic feasibility in isolation. This study develops an integrated physics-guided machine learning framework that combines laboratory turbine characterization, physically constrained regression modeling, and design-oriented techno-economic assessment for a 3 kW Pelton-type pico-hydroelectric system. Experiments conducted under heads of 40–80 m and discharges of 1.2–5.6 L s⁻¹ generated a 240-sample dataset used to train and validate five predictive models: a physics-based baseline, Ridge Regression (RR), Random Forest, Gradient Boosting, and a tuned Multi-Layer Perceptron. Using five-fold cross-validation and a 20 percent independent test set, RR achieved the highest accuracy (RMSE = 0.045 kW, R² = 0.992), with bootstrap uncertainty analysis confirming narrow confidence intervals and strong robustness under limited data. Coupling the validated RR model with hydraulic constraints enabled extrapolation to the 100 m design head, yielding a predicted output of 2.93 kW at 5 L s⁻¹, closely matching physics-based estimates. The techno-economic assessment produced an LCOE of 0.036 USD kWh⁻¹ and demonstrated positive NPV across sensitivity ranges. The proposed framework provides a physically interpretable, statistically reliable, and computationally efficient tool for performance prediction and feasibility evaluation of small-scale hydropower systems.