{"ID":2822822,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01410","arxiv_id":"2601.01410","title":"Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systems","abstract":"Accurate grid load forecasting is safety-critical: under-predictions risk supply shortfalls, while symmetric error metrics can mask this operational asymmetry. We introduce an operator-legible evaluation framework -- Under-Prediction Rate (UPR), tail $\\text{Reserve}_{99.5}^{\\%}$ requirements, and explicit inflation diagnostics ($\\text{Bias}_{24h}$/OPR) -- to quantify one-sided reliability risk beyond MAPE. Using this framework, we evaluate five neural architectures -- two state space models (S-Mamba, PowerMamba), two Transformers (iTransformer, PatchTST), an LSTM, and a probabilistic SSM variant (Mamba-ProbTSF) -- on a weather-aligned California Independent System Operator (CAISO) dataset spanning Nov 2023--Nov 2025 (84,498 hourly records across 5 regional transmission areas) under a rolling-origin walk-forward backtest. We develop and evaluate thermal-lag-aligned weather fusion strategies matched to each architecture's inductive bias. Our results demonstrate that standard accuracy metrics are insufficient proxies for operational safety: models with comparable MAPE can imply materially different tail reserve requirements ($\\text{Reserve}_{99.5}^{\\%}$). We show that explicit weather integration narrows error distributions, with the magnitude of improvement being architecturally determined -- iTransformer's cross-variate attention benefits significantly more than PatchTST's channel-independent design. Crucially, we identify a widespread susceptibility to \"fake safety\" in risk-averse forecasting: while probabilistic calibration reduces upper-tail errors, it achieves this by systematically inflating schedules (e.g., increasing bias by over 1,700 MW in severe cases) if left unconstrained. To solve this, we introduce Bias/OPR-constrained objectives that enable auditable trade-offs between minimizing tail risk and preventing trivial over-forecasting.","short_abstract":"Accurate grid load forecasting is safety-critical: under-predictions risk supply shortfalls, while symmetric error metrics can mask this operational asymmetry. We introduce an operator-legible evaluation framework -- Under-Prediction Rate (UPR), tail $\\text{Reserve}_{99.5}^{\\%}$ requirements, and explicit inflation dia...","url_abs":"https://arxiv.org/abs/2601.01410","url_pdf":"https://arxiv.org/pdf/2601.01410v6","authors":"[\"Sunki Hong\",\"Jisoo Lee\"]","published":"2026-01-04T07:30:50Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}