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Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

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In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression.

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arXiv Machine Learning
Jun 18

RNN(p) for Power Consumption Forecasting

arXiv:2209. 01378v3 Announce Type: replace Abstract: An elementary Recurrent Neural Network that operates on p time lags, called an RNN(p), is the natural generalisation of a linear autoregressive model ARX(p).

By Roberto Baviera, Pietro Manzoni