Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Khuntia, S.R. (TU Delft Intelligent Electrical Power Grids)
Rueda, José L. (TU Delft Intelligent Electrical Power Grids)
van der Meijden, M.A.M.M. (TU Delft Intelligent Electrical Power Grids; TenneT TSO B.V.)

2016

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.

Error analysis
forecasting
forecast error
load forecast uncertainty
neural network
short-term load forecast

http://resolver.tudelft.nl/uuid:b90c2f99-6113-4422-a796-aa7e2ac6224c

https://doi.org/10.1109/IJCNN.2016.7727854

IEEE, Piscataway, NJ

978-1-5090-0620-5

2016 International Joint Conference on Neural Networks (IJCNN)

IJCNN 2016, 2016-07-24 → 2016-07-29, Vancouver, Canada

Institutional Repository

conference paper

© 2016 S.R. Khuntia, José L. Rueda, M.A.M.M. van der Meijden