With the deep penetration of non-grid connected renewable generation and storage, electric vehicle charging, smart load control and time-of-use rates now come with greater load volatility. This in turn leads to eroding operational load forecast performance. To improve system operator’s confidence with the load forecasting process, there has been a movement toward developing and presenting an ensemble of load forecasts, which could include forecasts designed to handle the impact of rooftop solar PV and electric vehicle charging, forecasts that incorporate the impact of TOU pricing and smart load control, and load forecasts produced under alternative weather forecasts.

If the alternative load forecasts are clustered closely around each other then system operations may have greater confidence in the system conditions predicted by the ensemble. On the other hand, a forecast ensemble with a wide range could raise doubts about the forecasted system conditions, leading to system operators taking actions to hedge against the worst-case scenario. In effect, the forecast ensemble quantifies the plausible range of loads that are given uncertainty around future meteorological conditions such as temperatures, wind and solar conditions, and uncertainty around price sensitive loads and load control actions.

Within this new world of ensemble forecasting, there remains the reality that most downstream applications (e.g. transmission and distribution energy management systems and market models) require as an input only one load forecast. This means the load forecasting process needs a way of combing the alternative forecasts into a single “optimal” forecast that is then used for downstream processing.

Much of the academic literature on combining forecasts starts with the seminal paper “The Combination of Forecasts”, by J. M. Bates and C. W. J. Granger (source Operational Research Society, Vol. 20, No. 4 (Dec. 1969), pp.451-469). Bates and Granger start their analysis by observing that a combined forecast formed by taking a simple average of two alternative forecasts outperforms the two alternative forecasts. They then asked, if a 50/50 weighting of two independently generated forecasts can improve the overall forecast performance, is it possible to define a method for computing an “optimal” weighting scheme that leads to a combined forecast with the smallest mean squared forecast error?

I have drafted a white paper that provides a high-level review of some of the academic literature on combining forecasts and put forth a recommendation for how to develop an optimal forecast given an ensemble of alternative load forecasts. I am looking for comments on the white paper draft. If you are interested in providing your comments, please send an email to frank.monforte@itron.com.

The final white paper should be available later in 2020.

Dr. Frank A. Monforte on EmailDr. Frank A. Monforte on Linkedin
Dr. Frank A. Monforte
Director of Forecasting Solutions - Itron
Dr. Frank A. Monforte is Director of Forecasting Solutions at Itron, where he is an internationally recognized authority in the areas of real-time load and generation forecasting, retail portfolio forecasting, and long-term energy forecasting. Dr. Monforte’s real-time forecasting expertise includes authoring the load forecasting models used to support real-time system operations for the North American system operators, the California ISO, the New York ISO, the Midwest ISO, ERCOT, the IESO, and the Australian system operators AEMO and Western Power. Recent efforts include authoring embedded solar, solar plant, and wind farm generation forecast models used to support real-time operations at the California ISO. Dr. Monforte founded the annual ISO/TSO Forecasting Summit that brings together ISO/TSO forecasters from around the world to discuss forecasting challenges unique to their organizations. He directs the implementation of Itron’s Retail Forecasting System, including efforts for energy retailers operating in the United Kingdom, Netherlands, France, Belgium, Italy, Australia, and the U.S. These systems produce energy forecasts for retail portfolios of interval metered and non-interval metered customers. The forecast models he has developed support forecasting of power, gas and heat demand and forecasting of wind, solar, landfill gas, and mine gas generation. Dr. Monforte presides over the annual Itron European Energy Forecasting Group meeting that brings together European Energy Forecasters for an open exchange of ideas and solutions. Dr. Monforte directed the development of Itron’s Statistically Adjusted End-Use Forecasting model and supporting data. He founded the Energy Forecasting Group, which directs primary research in the area of long-run end-use forecasting. Recent efforts include designing economic indices that provide long-run forecast stability during periods of economic uncertainty. Email Frank at frank.monforte@itron.com, or click here to connect on LinkedIn.