Priority-Weighted Clustering and Prediction Intervals for AI-Driven Biogas Energy Forecasting

Biogas plays an increasingly important role in advancing decarbonisation goals, particularly in rural regions where livestock and agricultural waste can be converted into renewable energy. However, predicting annual farm-level biogas electricity output remains challenging due to operational variability, uncertain co-digestion practices, and external constraints such as grid integration and environmental disruptions. Traditional point prediction models often provide limited support for practical decision-making because they do not explicitly represent uncertainty around the estimated output. To address this limitation, this paper proposes a data-driven prediction interval framework for annual farm-level biogas electricity-output estimation. The study does not model future temporal horizons; rather, it estimates annual electricity generation at the farm level and constructs prediction intervals around these estimates. The proposed framework combines Self-Organising Map (SOM)-based clustering with Lower Upper Bound Estimation (LUBE) to account for operational heterogeneity among biogas-producing farms. SOM clustering is performed using pre-prediction operational covariates, specifically cattle population and co-digestion status, while electricity output is used only as the prediction target and for post-hoc interpretation. For each operational cluster, a neural prediction interval model is trained using the LUBE approach. A priority-weighted extension is then incorporated to reflect cluster-level operational or strategic importance in the evaluation of interval performance. Experiments on a real-world dataset from U.S. biogas systems show that the proposed framework can support a trade-off between interval width and coverage performance across standard confidence levels. By combining operational clustering with uncertainty-aware interval estimation, the method improves the interpretability and practical relevance of annual farm-level biogas electricity-output prediction for infrastructure planning.