Optimizing Human-Powered Energy Generation Using Gaussian Process Regression

This protocol presents a Gaussian Process Regression (GPR) optimization framework for human-powered electricity generation systems to address technological unemployment and renewable energy demands simultaneously. Seven participants performed 112 trials across 16 configurations combining four battery voltages (12 V, 24 V, 36 V, 48 V) with four electrical loads (10 W, 30 W, 50 W, 70 W) using a modified stationary bicycle generator. The GPR model incorporated automatic relevance determination (ARD) squared exponential kernels to map pedaling speed, pressure, voltage, and load parameters to generation efficiency while quantifying prediction uncertainty. Real-time data acquisition at 2 Hz captured mechanical and electrical parameters through Hall-effect sensors, strain gauges, and power analyzers with Modbus communication protocols. The optimized system achieved 80%-90% theoretical maximum efficiency with coefficient of variation (CoV) below 15%, compared to 60%-70% efficiency and 25%-35% variability in commercial systems. GPR predictions demonstrated R² = 0.713 with sub-10ms latency through sparse approximation techniques, enabling real-time control. Optimal operational parameters varied systematically: low loads required 150-180 revolutions per minute (RPM) at 50-70 N pressure, while higher loads peaked at 100-120 RPM with 100-150 N pressure. The 48 V configuration improved efficiency by 68% over 12 V baseline (measured as relative efficiency index, REI, normalized to baseline = 100%). Phase-specific monitoring identified fatigue onset at 35-40 min, triggering risk-aware control adjustments based on posterior uncertainty. The protocol establishes quantitative methods for balancing energy generation with physiological constraints, providing a reproducible framework for deploying human-powered systems in fitness facilities where break-even occurs at $0.18/kWh electricity pricing.