Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014 |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| 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 |
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Keywords
- power demand
- demand forecasting
- load modeling
- prediction algorithms
- artificial neural networks
Cite this
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Assessment of some methods for short-term load forecasting. / Koponen, Pekka; Mutanen, A.; Niska, Harri.
Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014. Institute of Electrical and Electronic Engineers IEEE, 2014.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Assessment of some methods for short-term load forecasting
AU - Koponen, Pekka
AU - Mutanen, A.
AU - Niska, Harri
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - power demand
KW - demand forecasting
KW - load modeling
KW - prediction algorithms
KW - artificial neural networks
U2 - 10.1109/ISGTEurope.2014.7028901
DO - 10.1109/ISGTEurope.2014.7028901
M3 - Conference article in proceedings
BT - Proceedings
PB - Institute of Electrical and Electronic Engineers IEEE
ER -