Temperature Scenario Generation for Probabilistic Load Forecasting

In today's dynamic and competitive business environment, probabilistic load forecasting is becoming increasingly important to utilities for quantifying the uncertainties in the future. Among the various approaches to generating probabilistic load forecasts, feeding simulated weather scenarios to a point load forecasting model is being commonly accepted by the industry for its simplicity and interpretability. There are three practical and widely used methods for temperature scenario generation, namely fixed-date, shifted-date, and bootstrap methods. Nevertheless, these methods have been used mainly on an ad hoc basis without being formally compared or quantitatively evaluated. For instance, it has never been clear to the industry how many years of weather history is sufficient to adopt these methods. This is the first study to evaluate these three temperature scenario generation methods based on the quantile score, a comprehensive quantitative error measure for probabilistic forecasts. Through a series of empirical studies on both linear and nonlinear models with three different levels of predictive power, we find that 1) the quantile score of each method shows diminishing improvement as the length of available temperature history increases; 2) while shifting dates can compensate short weather history, the quantile score improvement gained from the shifted-date method diminishes and eventually becomes negative as the number of shifted days increases; and 3) comparing with the fixed-date method, the bootstrap method offers the capability of generating more comprehensive scenarios but does not improve the quantile score. At the end, an empirical formula for selecting and applying the temperature scenario generation methods is proposed together with a practical guideline.