Akademik Məlumat İdarəetmə Sistemi
Advanced Engineering Informatics, vol.69, 2026 (SCI-Expanded, Scopus)
Accurate and reliable interval prediction is essential for dispatch, resource planning, and risk management in energy and power systems. However, prevailing approaches often suffer from feature redundancy, inadequate treatment of temporal dependencies, and poorly calibrated prediction intervals. To address these limitations, this paper proposes DFS-FDP-FQAR, an interval prediction framework that integrates dynamic feature selection (DFS), frequency-domain prediction (FDP), and fuzzy quantile association rules (FQARs). The framework dynamically selects informative features to mitigate redundancy, leverages time–frequency transforms to capture complex temporal regularities, and constructs adaptive, better-calibrated intervals via fuzzy association rules. In the point-forecasting stage, the FDP module couples a long short-term memory (LSTM) backbone with a sparse-attention mechanism: the LSTM models periodic components in the real part, while sparse attention accentuates abrupt signals in the imaginary part, thereby enhancing expressiveness. In the interval-forecasting stage, an FQAR procedure based on residuals and quantile-level fuzzy memberships produces data-adaptive, well-calibrated prediction intervals. Extensive experiments on multiple real-world power-energy datasets demonstrate superior point accuracy and interval reliability relative to state-of-the-art baselines. Specifically, the proposed model achieves a prediction interval coverage probability (PICP) from 95.84% to 96.96%, while maintaining a prediction interval normalized average width (PINAW) between 0.0800 and 0.3992. These results highlight the robustness and practicality of the model in engineering applications, outperforming existing benchmark models in terms of both coverage probability and interval width, and validating the effectiveness of the theoretical innovations.