Abstract
In this paper, we explore and compare three recently proposed Automated Machine Learning (AutoML) tools (AutoGluon, H\(_2\)O, Oracle AutoMLx) to create a single regression model that is capable of predicting smart city energy building consumption values. Using a recently collected one year hourly energy consumption dataset, related with 29 buildings from a Portuguese city, we perform several Machine Learning (ML) computational experiments, assuming two sets of input features (with and without lagged data) and a realistic rolling window evaluation. Furthermore, the obtained results are compared with a univariate Time Series Forecasting (TSF) approach, based on the automated FEDOT tool, which requires generating a predictive model for each building. Overall, competitive results, in terms of both predictive and computational effort performances, were obtained by the input lagged AutoGluon single regression modeling approach.
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This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020.
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Soares, D., Pereira, P.J., Cortez, P., Gonçalves, C. (2023). A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_25
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