Automated Machine Learning for Short-term Electric Load Forecasting

From detecting skin cancer to translating languages and 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 consumption of electricity. 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 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. These benchmarks are derived from real world load consumption tasks, namely household consumption from the UCI data repository and consumption data from an industrial office building. Our experimental results indicate great potential for improving the accuracy of energy consumption prediction by using automated machine learning approaches.