Power grid forecasting tool reduces costly errors

A new tool that is said to increase the accuracy of forecasting future electricity needs by up to 50%, and may also have the potential to save millions in wasted energy costs, has been developed by researchers at the US Department of Energy’s Pacific Northwest National Laboratory (PNNL).

Accurately forecasting future electricity needs can be difficult due to sudden weather changes or other variables impacting projections minute by minute. The Power Model Integrator has been designed to assist with addressing costly errors that can lead to serious impacts, from blackouts to high market costs.

Performance of the tool was tested against five commonly used forecasting models processing a year’s worth of historical power system data.

“For forecasts one to four hours out, we saw a 30–55% reduction in errors,” said Luke Gosink, scientist and project lead at PNNL.

“It was with longer-term forecasts — the most difficult to accurately make — where we found the tool actually performed best.”

Fluctuations in energy demand throughout the day, season and year along with weather events and increased use of intermittent renewable energy from the sun and wind all contribute to forecasting errors. Miscalculations can be costly, put stress on power generators and lead to instabilities in the power system.

Grid coordinators have the daily challenge of forecasting the need for and scheduling exchanges of power to and from a number of neighbouring entities. The sum of these future transactions — the net interchange schedule — is submitted and committed to in advance. Accurate forecasting of the schedule is critical not only to grid stability, but a power purchaser’s bottom line.

“Imagine the complexity for coordinators at regional transmission organisations who must accurately predict electricity needs for multiple entities across several states,” Gosink said.

“Our aim was to put better tools in their hands.”

Currently, forecasters rely on a combination of personal experience, historical data and often a preferred forecasting model. Each model tends to excel at capturing certain grid behaviour characteristics, but not necessarily the whole picture.

To address this gap, PNNL researchers theorised that they could develop a method to guide the selection of an ensemble of models with the ideal, collective set of attributes in response to what was occurring on the grid at any given moment.

The resulting tool has the ability to adaptively combine the strengths of different forecasting models continuously and in real time to address a variety scenarios that impact electricity use, from peak periods during the day to seasonal swings. To do this, the tool accesses short- and long-term trends on the grid as well as the historical forecasting performance of the individual and combined models. Minute by minute, the system adapts to and accounts for this information to form the best aggregated forecast possible at any given time.

“The underlying framework is very adaptable, so we envision using it to create other forecasting tools for electric industry use,” Gosink said.

“We also are exploring other applications, from the prediction of chemical properties studied in computational chemistry applications to the identification of particles for high-energy physics experiments.”

For further information on the Power Model Integrator, go to: http://energyenvironment.pnnl.gov/pdf/BMA_NIS_final.pdf.

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Image courtesy of Pacific Northwest National Laboratory - PNNL under CC