Format of Original
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Power and Energy Society General Meeting (PESGM)
This paper presents a novel detrending algorithm that allows long-term natural gas demand signals to be used effectively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent the range of consumption patterns necessary for accurate short-term forecasting. In contrast, longer data sets present a wide range of customer characteristics, but their long-term historical trends must be adjusted to resemble recent data before models can be developed. Our approach detrends historical natural gas data using domain knowledge. Forecasting models trained on data detrended using our algorithm are more accurate than models trained using nondetrended data or data detrended by benchmark methods. Forecasting accuracy improves using detrended longer-term signals, while forecast accuracy decreases using non-detrended long-term signals.
Brown, Ronald H.; Vitullo, Steven; Corliss, George F.; Adya, Monica; Kaefer, Paul E.; and Povinelli, Richard J., "Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts" (2015). Electrical and Computer Engineering Faculty Research and Publications. 142.
Accepted version. Published as part of the proceedings of the conference, IEEE Power and Energy Society General Meeting (PESGM), 2015: 1-5. DOI. © 2015 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.