@inproceedings{3cff76b1545045f39ec7a20d702861d9,
title = "Assessment of some methods for short-term load forecasting",
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.",
keywords = "power demand, demand forecasting, load modeling, prediction algorithms, artificial neural networks",
author = "Pekka Koponen and Antti Mutanen and Harri Niska",
year = "2014",
doi = "10.1109/ISGTEurope.2014.7028901",
language = "English",
series = "IEEE PES Innovative Smart Grid Technologies Conference Europe",
publisher = "IEEE Institute of Electrical and Electronic Engineers",
booktitle = "IEEE PES Innovative Smart Grid Technologies, Europe",
address = "United States",
note = "5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014, ISGT-Europe 2014 ; Conference date: 12-10-2014 Through 15-10-2014",
}