Abstract
Accurate forecasting of loads is essential for smart
grids and energy markets. This paper compares the
performance of the following models in short-term load
forecasting: 1) smart metering data based profile models,
2) a neural network (NN) model, and 3) a Kalman-filter
based predictor with input nonlinearities and a
physically based main structure. The comparison helps
method selection for the development of hybrid models for
forecasting the load control responses. According to the
results all these three modeling approaches show much
better performance than 4) the traditional load profiles
and 5) a static outdoor temperature dependency model
applied with a lag. The neural network model was the most
accurate in the comparison, but the differences of the
three methods developed were rather small and also other
aspects and other methods must be considered and compared
when selecting the method for a specific purpose.
Original language | English |
---|---|
Title of host publication | Proceedings |
Subtitle of host publication | IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014 |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Number of pages | 6 |
ISBN (Electronic) | 978-1-4799-7720-8 |
DOIs | |
Publication status | Published - 2014 |
MoE publication type | A4 Article in a conference publication |
Event | 5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014 - Istanbul, Turkey Duration: 12 Oct 2014 → 15 Oct 2014 |
Conference
Conference | 5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014 |
---|---|
Abbreviated title | ISGT-Europe 2014 |
Country | Turkey |
City | Istanbul |
Period | 12/10/14 → 15/10/14 |
Keywords
- power demand
- demand forecasting
- load modeling
- prediction algorithms
- artificial neural networks