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Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck

TL;DR

The paper addresses miscalibration in probabilistic renewable forecasts caused by nonstationarity and covariate shifts, aiming to provide reliable uncertainty quantification. It proposes Context-Aware Conformal Prediction (CACP), which reweights calibration samples by similarity in auxiliary covariates to achieve calibrated and sharp prediction intervals under Conformal Prediction theory. The approach introduces kernel, kmeans, and knn weighting schemes, contextual features such as Historical Actuals, Time Embeddings, and Normalized Time of Solar Day, and a dynamic hypertuning protocol, evaluated on day-ahead solar forecasts for MISO, SPP, and ERCOT. Results show empirical coverage approaching the target $1-\alpha$ with reduced interval widths compared to baselines, demonstrating improved reliability and robustness for grid operations.

Abstract

Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines.

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

TL;DR

The paper addresses miscalibration in probabilistic renewable forecasts caused by nonstationarity and covariate shifts, aiming to provide reliable uncertainty quantification. It proposes Context-Aware Conformal Prediction (CACP), which reweights calibration samples by similarity in auxiliary covariates to achieve calibrated and sharp prediction intervals under Conformal Prediction theory. The approach introduces kernel, kmeans, and knn weighting schemes, contextual features such as Historical Actuals, Time Embeddings, and Normalized Time of Solar Day, and a dynamic hypertuning protocol, evaluated on day-ahead solar forecasts for MISO, SPP, and ERCOT. Results show empirical coverage approaching the target with reduced interval widths compared to baselines, demonstrating improved reliability and robustness for grid operations.

Abstract

Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines.
Paper Structure (29 sections, 20 equations, 4 figures, 4 tables)

This paper contains 29 sections, 20 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction and Related Works
  2. Motivation
  3. Literature Review
  4. Contributions and Outline
  5. Background Material
  6. Problem Definition
  7. Conformalized Quantile Regression (CQR)
  8. Context-Aware Conformal Prediction
  9. Weighted Conformal Prediction
  10. Kernel
  11. KMeans
  12. KNN
  13. Context-Aware Features
  14. Historical Actuals
  15. Time Embeddings
  16. ...and 14 more sections

Figures (4)

  • Figure 1: Illustration of the proposed CACP framework. Conformity scores from the calibration set are weighted based on their similarity to the target prediction point—illustrated in purple and orange. While CQR assigns uniform weights to all samples, CACP emphasizes more similar instances, resulting in tighter and more efficient prediction intervals.
  • Figure 2: Coverage vs. AIW trade-off for different CP methods across system-level forecasts in three ISOs. CACP methods consistently achieve lower average interval widths compared with baselines at similar coverage rates.
  • Figure 3: Conditional coverage for different hours of the day across three ISO system-level forecasts (SPP, MISO, and ERCOT) at target marginal coverage $80\%$. CACP-based methods maintain coverage close to the target across all hours, whereas baseline methods tend to under-cover during early morning and evening periods, and over-cover near midday peak hours.
  • Figure 4: Distribution of conformity scores for the MISO system-level forecasts using CQR and CACP at two representative time steps (12pm and 6pm) on 2019-04-2. While the CQR distribution remains relatively stable across hours, the CACP distribution varies with time, showing a right-skewed (more positive) pattern in the evening (6pm) similar to CQR, and a left-shifted (more negative) distribution at noon (12pm).