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Dynamic energy system modeling using hybrid physics-based and machinelearning encoder–decoder models

Energy and AI 2022年第3期9卷 128-138页

作者： Derek Machalek Jake Tuttle Klas Andersson Kody M.Powell Department of Chemical Engineering University of UtahSalt Lake CityUTUnited States of America taber international LLCUnited States of America Department of Space Earthand EnvironmentUniversity of ChalmersGothenburgSweden Department of Mechanical Engineering University of UtahSalt Lake CityUTUnited States of America

Three model configurations are presented for multi-step time series predictions of the heat absorbed by thewater and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into thefuture, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, anovel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. Thehybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, waterwall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared totarget values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. Thehybrid model with the incomplete dataset highlights the importance of the manipulated variables to the *** hybrid model, compared to the pure machine learning model, is over 10% more accurate on averageover 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a localsensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in theboiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

关键词： Hybrid model Encoder-decoder Time series Automatic differentiation Thermal power plant

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