Document Type
Conference Proceeding
Language
eng
Publication Date
2015
Publisher
International Institute of Forecasters
Source Publication
International Symposium on Forecasting
Abstract
Energy utilities see higher risk when forecasting for their operating areas (service territories) on days that are high-demand or difficult to forecast. These days often have unusual weather patterns (e.g., colder than normal or significant temperature fluctuation from previous days). Due to their unusual nature, data describing these days are scarce. We present a method that successfully transforms natural gas consumption data from operating areas in vastly different geographic regions and climates, with different customer bases, to make better forecasts for areas that have insufficient historical data. Our surrogate data transformation algorithm results in higher forecast accuracy, thereby reducing the risk to energy utilities.
Recommended Citation
Kaefer, Paul E.; Ishola, Babatunde; Corliss, George F.; and Brown, Ronald H., "Using Surrogate Data to Mitigate the Risks of Natural Gas Forecasting on Unusual Days" (2015). Electrical and Computer Engineering Faculty Research and Publications. 158.
https://epublications.marquette.edu/electric_fac/158
Comments
Published version. Published as part of the proceedings of the 35th International Symposium on Forecasting, 2015. Publisher link. © 2015 International Institute of Forecasters. Used with permission.