Ensemble Score Filtering for Real-Data Energy Consumption Forecast Correction

2026-05-27

AI Summary

This study addresses the challenge of unreliable long-term forecasting in real-world energy consumption data, which is often compromised by missing values, noise, or latency. To tackle this issue, the authors propose a sequential assimilation framework that integrates a pre-trained spatiotemporal prediction model with an ensemble score-based filter (EnSF). Innovatively, they introduce score-based diffusion models into energy data assimilation, leveraging closed-form score expressions to avoid retraining neural networks and thereby substantially enhancing state correction capabilities under nonlinear observations. Experimental results demonstrate that, compared to open-loop prediction and the ensemble Kalman filter (EnKF), the proposed EnSF method achieves superior accuracy and robustness in long-term state estimation for high-dimensional energy systems.

Abstract

Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, however, complete and timely measurements may not always be available, and the observed data can be partial, noisy, or delayed. This motivates the use of learned forecasting models for predicting the evolving consumption state, together with data assimilation methods for sequential forecast correction. In this work, we study a high-dimensional data assimilation problem for real energy-consumption data. \modeltext{The forward prediction is supplied by a pretrained black-box spatio-temporal forecasting model, which is treated as the state propagator in the filtering procedure.} We employ the Ensemble Score Filter (EnSF) to assimilate partial and noisy observations and to correct the forecast trajectory over time. The EnSF uses score-based diffusion models to approximate filtering distributions and avoids retraining neural-network score models during assimilation by using a closed-form score representation and Monte Carlo approximation. Numerical experiments demonstrate that open-loop propagation of the learned forecasting model can become unreliable over long horizons, while EnSF-based correction substantially improves state estimation. Comparisons with the Ensemble Kalman Filter (EnKF) further show that EnSF provides stronger correction under the nonlinear observation setting considered in this work.

Problem

• energy consumption forecasting
• data assimilation
• partial observations
• noisy data
• forecast correction

Innovation

• Ensemble Score Filter
• score-based diffusion models
• data assimilation
• energy consumption forecasting
• nonlinear observation