Unveiling and estimating behind-the-meter rooftop solar self-consumption using explainable AI

As renewable energy adoption grows, rooftop solar for self-consumption is also increasing. This “behind-the-meter” self-consumption is usually unmeasured, making grid operation difficult. However, few studies have estimated self-consumption. This study analyzes the impact of weather and proposes a framework for estimating self-consumption at grid using readily available data. Employing machine learning with XAI, the downward impact of solar radiation on grid demand is quantified to estimate self-consumption. Using actual Australia data, self-consumption was estimated under different solar adoption levels, demonstrating the high accuracy and versatility. Additionally, the increase in self-consumption in summer was quantified by the interaction between temperature and radiation. In summer, peak hourly self-consumption increases linearly by 30% for every 10 °C rise in daily maximum temperature due to cooling demand. As cooling demand is expected to grow, this finding has significant implications. This study is globally applicable and valuable for grid operators and policymakers integrating renewables.