@inproceedings {pub4031,
	title = {Automated Machine Learning for Short-term Electric Load Forecasting},
	author = {Can Wang AND Steffen Limmer AND Mitra Baratchi AND Thomas B{\"a}ck AND Holger Hoos AND Markus Olhofer},
	year = {2019},
	month = {December},
	abstract = {From detecting skin cancer, to translating languages, to forecasting electricity consumption, machine learning is enabling advanced capabilities of computer systems across a broad
range of important real-world applications. In this work, we present machine learning models for forecasting the electricity consumption. Short-term electric load forecasting has been a
fundamental concern in power operation systems for over a century. Energy load forecasting is of even greater importance due to its applications in the planning of demand side management, smart electric vehicles and other smart grid technologies. We use two state-of-the-art automated machine learning systems (auto-sklearn and TPOT), which automate model selection and
hyperparameter optimization, to achieve maximum prediction accuracy, and compare their performance for the task of load prediction using two benchmark problems. The two benchmarks
are both real world load consumption, one is household consumption from UCI data repository and the other is industry consumption from an office building. Our experimental results indicate great potential to improve the accuracy of the energy consumption prediction.},
	publisher = {IEEE},
	booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI) 2019}
}