Supervised Machine Learning for Renewable Energy

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

Accurate renewable energy forecasting is important for optimizing grid integration and advancing environmental sustainability. This chapter develops predictive models based on supervised machine learning for solar energy consumption using historical data from solar power plants, integrating various data sources: historical energy consumption, actual weather conditions (including temperature, insolation, and wind speed), and historical weather forecasts. Advanced artificial intelligence and machine learning algorithms including deep learning were trained on multi-source dataset to identify complex temporal patterns and weather-energy patterns. The models achieved high precision, demonstrating robustness against meteorological variability. Accurate predictive models enable utilities to reduce fossil-fuel-based reserve capacity, minimize grid inefficiencies, and enhance renewable energy utilization. For environmental sustainability, these models directly support decarbonization goals by enabling larger solar integration, reducing associated carbon emissions from backup generation, and promoting resource-efficient energy planning. By facilitating the reliable and efficient integration of solar power, this approach represents a small step toward achieving zero net emissions in the energy sector.

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