Model predictive control based on deep learning for solar parabolic-trough plants
نویسندگان
چکیده
In solar parabolic-trough plants, the use of Model Predictive Control (MPC) increases output thermal power. However, MPC has disadvantage a high computational demand that hinders its application to some processes. This work proposes using artificial neural networks approximate optimal flow rate given by an controller decrease load drastically 3% computation time. The have been trained 30-day synthetic dataset collector field controlled MPC. different number measurements as inputs network analyzed. results show controllers provide practically same mean power with differences under 0.02 kW for most networks, less abrupt changes at and slight violations constraints. Moreover, proposed perform well, even low sensors predictions, decreasing 10% original size. • Artificial in plant. learns outputs offline from approach performance MPC, fewer predictions. are much faster than computable real-time. behaviour
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ژورنال
عنوان ژورنال: Renewable Energy
سال: 2021
ISSN: ['0960-1481', '1879-0682']
DOI: https://doi.org/10.1016/j.renene.2021.08.058