Short-Term Probabilistic Interval Forecasting of Electricity, Cooling, and Heating Loads Using an Improved Osprey Optimization Algorithm-Based TCN-BiLSTM-QR Model

Integrated Energy Systems (IESs) are characterized by strong nonstationarity, seasonal heterogeneity, and stochastic fluctuations in electric, cooling, and heating loads, making accurate and reliable probabilistic forecasting challenging. To address this issue, this study proposes an Improved Osprey Optimization Algorithm-based TCN-BiLSTM-Quantile Regression (IOOA-TCN-BiLSTM-QR) model for short-term probabilistic forecasting of multi-energy loads. The model integrates Logistic chaotic mapping and Lévy flight to enhance the optimization capability of the Osprey Optimization Algorithm, while TCN and BiLSTM are employed to capture multiscale local features and long-term temporal dependencies, respectively. Quantile regression is further introduced to construct 90% prediction intervals and quantify forecasting uncertainty. Using year-round hourly operational data from a university IES, the proposed model achieves average PICP and PINAW values of 90.50% and 0.1513, respectively, indicating a favorable balance between interval coverage and width. For point forecasting, the model achieves test-set R2 values of 0.9385, 0.9781, and 0.9731 for electric, cooling, and heating loads, respectively, with corresponding MAPE values of 2.61%, 2.67%, and 2.36%. Comparative and ablation experiments demonstrate the effectiveness of the integrated optimization and temporal feature extraction framework, while cross-dataset validation further confirms its applicability to different multi-energy load conditions. Overall, the proposed model provides accurate point forecasts and 90% prediction intervals with average coverage close to the nominal level, offering quantitative information for reserve capacity allocation, operational scheduling, and risk-aware decision-making in IESs.