Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network

• URL: http://arxiv.org/abs/2311.07202v6
• Date: Tue, 10 Sep 2024 06:19:52 GMT
• Title: Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network
• Authors: Zihao Wang, Donghan Yu, Zhe Wu,
• Abstract summary: We present a novel input memory-based neural network-based optimization for energy and chemical systems. We demonstrate the superior performance of ICLSTM-based optimization in terms of runtime. Specifically, in a real-time optimization problem of a real-world energy system at LHT Holdings in Singapore, IC-LSTM-based optimization achieved at least 4-fold compared to conventional LSTM-based optimization.
• Score: 18.84965086425835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural network-based optimization and control methods, often referred to as black-box approaches, are increasingly gaining attention in energy and manufacturing systems, particularly in situations where first-principles models are either unavailable or inaccurate. However, their non-convex nature significantly slows down the optimization and control processes, limiting their application in real-time decision-making processes. To address this challenge, we propose a novel Input Convex Long Short-Term Memory (IC-LSTM) network to enhance the computational efficiency of neural network-based optimization. Through two case studies employing real-time neural network-based optimization for optimizing energy and chemical systems, we demonstrate the superior performance of IC-LSTM-based optimization in terms of runtime. Specifically, in a real-time optimization problem of a real-world solar photovoltaic energy system at LHT Holdings in Singapore, IC-LSTM-based optimization achieved at least 4-fold speedup compared to conventional LSTM-based optimization. These results highlight the potential of IC-LSTM networks to significantly enhance the efficiency of neural network-based optimization and control in practical applications. Source code is available at https://github.com/killingbear999/ICLSTM.

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