Can Wang, Steffen Limmer, Mitra Baratchi, Thomas Bäck, Holger Hoos, Markus Olhofer, "Automated Machine Learning for Short-term Electric Load Forecasting", IEEE Symposium Series on Computational Intelligence (SSCI) 2019, 2019.

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.