Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models
Journal article, 2022

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, 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 to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

Thermal power plant

Encoder–decoder

Hybrid model

Time series

Automatic differentiation

Author

Derek Machalek

University of Utah

Jacob F. Tuttle

Taber International

Klas Andersson

Chalmers, Space, Earth and Environment, Energy Technology

University of Utah

Kody M. Powell

University of Utah

Energy and AI

26665468 (eISSN)

Vol. 9 100172

Driving Forces

Sustainable development

Subject Categories

Energy Engineering

Probability Theory and Statistics

Control Engineering

Areas of Advance

Energy

DOI

10.1016/j.egyai.2022.100172

More information

Latest update

1/3/2024 9