Forecasting
Toward an Optimal Combined Load Forecast for System Operations
April 21, 2020
Deep penetration of non-grid connected renewable generation and storage, electric vehicle charging, smart load control and time-of-use rates create greater load volatility, which in turn, leads to eroding operational load forecast performance. To improve the system operator’s confidence with the load forecasting process, there has been a movement toward developing and presenting an ensemble of load forecasts.
The ensemble could include forecasts designed to handle the impact of rooftop solar PV and electric vehicle charging, forecasts that incorporate the impact of time-of-use 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 given the uncertainty around future meteorological conditions such as temperatures, wind and solar conditions as well as 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 a single load forecast as an input. 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.
Dr. Frank A. Monforte has authored a white paper that provides a high-level review of some of the econometric/operations research and data science literature on combining forecasts, and puts forth a recommendation for how to develop an optimal forecast specific to the problem of operational load forecasting.
To read the paper, go to our forecasting page at http://www.itron.com/forecasting.
The ensemble could include forecasts designed to handle the impact of rooftop solar PV and electric vehicle charging, forecasts that incorporate the impact of time-of-use 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 given the uncertainty around future meteorological conditions such as temperatures, wind and solar conditions as well as 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 a single load forecast as an input. 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.
Dr. Frank A. Monforte has authored a white paper that provides a high-level review of some of the econometric/operations research and data science literature on combining forecasts, and puts forth a recommendation for how to develop an optimal forecast specific to the problem of operational load forecasting.
To read the paper, go to our forecasting page at http://www.itron.com/forecasting.
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