Load Profile Generation for Robust Optimization: A Stochastic Approach Based on Conditional Probability Approximation

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Abstract

With the growing integration of renewable energy sources into distributed grids, accurate household‐level load forecasting becomes essential for robust energy management and optimization. This paper proposes a lightweight stochastic profile generation method grounded in conditional probability approximation. First, empirical conditional distributions are mined from historical load data via hourly histogram binning and correlation analysis. Second, a Monte Carlo‐inspired “flock” of plausible future load trajectories is generated iteratively, each endowed with an occurrence probability. Validation on the Ausgrid dataset (127 prosumer profiles over one year) shows that the probabilistic mining step requires only 0.5–0.6 s for history depths of 30–180 days, while generating 200 scenarios takes merely 8.1 ms, with a total memory footprint of approximately 200 KB. These computational and storage efficiencies render the approach suitable for online deployment on edge devices, enabling robust optimization under uncertainty in renewable energy communities.

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