NDLI: An ensemble approach for forecasting net interchange schedule

Content Provider	IEEE Xplore Digital Library
Author	Vlachopoulou, M. Gosink, L. Pulsipher, T.C. Ferryman, T. Ning Zhou Jianzhong Tong
Copyright Year	2013
Description	Author affiliation: Pacific Northwest Nat. Lab., Richland, WA, USA (Vlachopoulou, M.; Gosink, L.; Pulsipher, T.C.; Ferryman, T.; Ning Zhou) \|\| PJM Interconnection RTO, Norristown, PA, USA (Jianzhong Tong)
Abstract	The net interchange schedule (NIS) is the sum of the transactions (MW) between an Independent System Operator/Regional Transmission Organization (ISO/RTO) and its neighbors. Effective forecasting of the submitted NIS can improve grid operation efficiency. This paper applies a Bayesian model averaging (BMA) technique to forecast submitted NIS. As an ensemble approach, the BMA method aggregates different forecasting models in order to improve forecasting accuracy and consistency. In this study, the BMA method is compared to two alternative approaches: a stepwise regression method and an artificial neural network (ANN) trained for NIS forecasting. In our comparative analysis, we use field measurement data from the PJM Interconnection RTO to train and test each method. Our preliminary results indicate that ensemble-based methods can provide more accurate and consistent NIS forecasts in comparison to non-ensemble alternate methods.
Sponsorship	IEEE Power Energy Soc.
Starting Page	1
Ending Page	5
File Size	383181
Page Count	5
File Format	PDF
ISBN	9781479913039
ISSN	19449925
DOI	10.1109/PESMG.2013.6672760
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Forecasting Predictive models Artificial neural networks Mathematical model Testing Vectors Bayes methods power grid operations Bayesian model averaging ensemble approach forecasting net interchange schedule
Content Type	Text
Resource Type	Article

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