Abstract

Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants. The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions. The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities, resulting in a reduction in the robustness of model-based optimization. In this paper, we leverage the input convex neural networks (ICNN) to identify the chiller model to construct a convex mapping between control variables and the objective function, which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading (OCL). Approximation performances are evaluated through a four-model comparison based on an experimental data set, and the statistical results show that, on the premise of retaining prior convexities, the proposed model depicts excellent approximation power for the data set, especially the unseen data. Finally, the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies. Compared with conventional and meta-heuristic methods, the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.

中文翻译：

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使用输入凸神经网络的面向优化的建模方法及其在最佳冷水机组负载中的应用

摘要

多冷水机组系统的优化是高效冷水机组运行不可或缺的方法。基于模型的优化算法获得的最优值取决于精确且可解的目标函数。尽管经典神经网络具有令人印象深刻的非线性拟合能力，但仍无法提供凸输入输出映射，从而导致基于模型的优化的鲁棒性降低。在本文中，我们利用输入凸神经网络（ICNN）来识别冷却器模型，以构建控制变量和目标函数之间的凸映射，这使得基于神经网络的OCL成为凸优化问题，并将其应用于多冷却器优化以实现最佳冷却器负载 (OCL)。基于实验数据集，通过四种模型的比较来评估逼近性能，统计结果表明，在保留先验凸性的前提下，该模型对数据集，尤其是未见过的数据表现出优异的逼近能力。最后，将 ICNN 模型应用于多冷水机系统的典型 OCL 问题，并结合三种类型的优化策略。与传统方法和元启发式方法相比，数值结果表明，基于梯度的 BFGS 算法在面对连续冷负荷输入时提供了更好的节能率，并且具有令人印象深刻的收敛速度。