Probabilistic Forecasting of Multi-Energy Loads in Integrated Energy Systems via Condition-Aware Dynamic Coupling Routing and Conformal Quantile Calibration

Robust Probabilistic Load Forecasting for Multi-Energy Buildings Using Issue-Aware and Conformally Calibrated Gradient Boosting

A practical contribution is provided in the form of a forecasting method that is not only accurate but also statistically reliable in estimating operational risk, thereby bridging the gap between industry demands for robust systems and the constraints imposed by real-world data quality.

Data-driven energy mix optimization: forecasting, risk-aware planning and quantum-enhanced methods

We propose a unified, risk-aware framework for renewable energy mix planning integrating meteorological parameterization, probabilistic machine learning forecasts and optimization. Using NASA POWER reanalysis, wind- and solar-relevant features are derived. Gaussian Process Regression provides point predictions with predictive covariances, capturing both variability and interdependence of renewable resources. These forecasts feed into a portfolio-theoretic optimization model: short-term scheduling is solved as mixed-integer quadratic programs with Alternating Direction Method of Multipliers (ADMM)-based decomposition, while long-term planning is formulated as binary quadratic programs with variance and lagged-covariance penalties. The exponential scaling of classical solvers motivates hybrid quantum–classical approaches such as quantum annealing for multi-period, high-resolution planning. The framework reduces exposure to scarcity events, enhances security of supply and directly supports decarbonization targets, while also offering a transferable methodology for risk-informed planning in other sectors facing stochastic variability. The results obtained from the implementation of the proposed framework for Germany indicate that endowed with quantum computing techniques the short-term optimization can facilitate robust integration of the renewables in the grid without compromising grid reliability and the long-term optimized planning (not feasible with classical computing resources) can avoid overproduction by about 30% at certain times, reducing storage costs and detrimental impact on grid frequency.

Multi-Timescale Risk-Aware Planning for High-Renewable Power Systems: A Review and Analytical Framework

New Energy Power Load Forecasting and multi-time Scale Scheduling Optimization Based on Informer-Transformer

To address the load uncertainty and limited dispatch flexibility inherent in power systems with high renewable energy penetration, this paper proposes an integrated architecture that combines the Informer prediction model with a multi-time-scale scheduling optimization strategy. Accurate forecasting and coordinated scheduling are increasingly important for maintaining the stability and efficiency of modern electromagnetic energy systems and smart grid infrastructures. The study first constructs a high-quality time series dataset from multi-source information, including load, renewable generation, and meteorological data, through systematic preprocessing. Leveraging the ProbSparse sparse attention mechanism, the Informer model performs high-precision rolling forecasting at minute-, hour-, and day-level time scales. A hierarchical multi-time-scale dispatch framework is then established to tightly couple prediction with control, covering day-ahead economic dispatch, hourly source–load balancing, and minute-level energy storage regulation. By integrating energy storage systems and enforcing operational constraints, the proposed framework jointly optimizes economic efficiency and system stability. Experimental results demonstrate that the proposed method achieves RMSE and MAE values of 0.152 and 0.118 for one-hour forecasting, significantly outperforming baseline approaches. In dispatch optimization, the operating cost is reduced to 4,281 yuan while the renewable energy curtailment rate decreases to 2.1%, indicating superior economy and robustness. The proposed framework provides an effective solution for intelligent operation of high-renewable power systems and offers valuable support for reliable electromagnetic energy management and sustainable grid operation.

Context-Aware Asymmetric Conformal Calibration of Renewable-Power Prediction Intervals for Day-Ahead Operational Risk Assessment

High renewable penetration makes day-ahead operation sensitive to the directional effects of wind and photovoltaic forecast errors. Conventional prediction intervals mainly evaluate coverage and sharpness, but lower- and upper-boundary violations correspond to different operational risks: shortage-side supply-adequacy pressure and accommodation-side curtailment pressure. This paper proposes context-aware asymmetric conformal quantile regression (CA-ACQR) to construct directional renewable-power risk intervals. The method builds separate conformal scores for the two interval sides, estimates context-dependent boundary corrections, and reallocates the tail-risk budget under supply-priority, balanced, and accommodation-priority profiles. Case studies use regional wind and photovoltaic power data, with contextual groups defined by renewable type, lead-time block, forecast difficulty, weather-risk regime, and output level. CA-ACQR increases the prediction interval coverage probability (PICP) from 91.11% to 94.07% and reduces the accommodation-side violation rate from 4.37% to 1.44%. The results demonstrate selectable directional risk postures and quantify trade-offs among interval width, directional violations, normalized stress cost, and the 95% conditional value-at-risk stress cost.

B-TGPRF: A Bayesian Temporal Graph Probabilistic Model for Calibrated Overload-Risk Forecasting in Power Transmission Grids

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R2 = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection.