Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systems

📝 Original Info

• Title: Reliable Grid Forecasting: State Space Models for Safety-Critical Energy Systems
• ArXiv ID: 2601.01410
• Date: 2026-01-04
• Authors: Sunki Hong, Jisoo Lee

📝 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 Reserve % 99.5 requirements, and explicit inflation diagnostics (Bias 24h /OPR)-to quantify one-sided reliability risk beyond MAPE. Using this framework, we evaluate state space models (Mamba variants) and strong baselines 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 for these architectures. Our results demonstrate that standard accuracy metrics are insufficient proxies for operational safety: models with comparable MAPE can imply materially different tail reserve requirements (Reserve % 99.5 ). We show that explicit weather integration narrows error distributions, reducing the impact of temperature-driven demand spikes. Furthermore, while probabilistic calibration reduces large-error events, it can induce systematic schedule inflation. We introduce Bias/OPRconstrained objecti...

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