Optimizing Human-Powered Energy Generation Using Gaussian Process Regression

Area of Science:

• Renewable Energy Systems
• Human-Computer Interaction
• Machine Learning Applications

Background:

• Growing demand for renewable energy sources.
• Need for sustainable solutions to technological unemployment.
• Limitations of current human-powered electricity generation systems.

Purpose of the Study:

• To develop and validate a Gaussian Process Regression (GPR) optimization framework for human-powered electricity generation.
• To simultaneously address technological unemployment and renewable energy demands.
• To establish quantitative methods for balancing energy generation with physiological constraints.

Main Methods:

• Gaussian Process Regression (GPR) with Automatic Relevance Determination (ARD) squared exponential kernels.
• Real-time data acquisition (2 Hz) of mechanical and electrical parameters.
• 112 trials across 16 configurations (4 battery voltages x 4 electrical loads) with 7 participants.

Main Results:

• Optimized system achieved 80%-90% theoretical maximum efficiency with coefficient of variation (CoV) below 15%.
• GPR predictions showed R² = 0.713 with sub-10ms latency, enabling real-time control.
• Identified optimal pedaling parameters (RPM, pressure) for different load conditions and fatigue onset.

Conclusions:

• The GPR framework provides a reproducible protocol for optimizing human-powered electricity generation.
• The system demonstrates significant efficiency gains and reduced variability compared to commercial systems.
• The protocol offers a viable approach for deploying human-powered systems in fitness facilities, achieving cost-effective electricity generation.