Real-time machine-learning-based optimization using Input Convex Long Short-Term Memory network

Zihao Wang, Donghan Yu and Zhe Wu

Applied Energy, 2025, vol. 377, issue PB, No S0306261924018555

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.

Keywords: Optimization; Deep learning; Input Convex Neural Networks; Computational efficiency; Nonlinear processes; Solar PV systems (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124472

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