A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

中文翻译：

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基于转移学习方法的可持续城市预测污水处理厂的能耗

当今社会的主要挑战是使大型城市中心更具可持续性。改善城市的各种基础设施的能源效率是改善城市可持续性的一个方面，其中废水处理厂（WWTP）就是其中之一。因此，本研究旨在构思，调整和评估一组候选深度学习模型，目标是采用递归多步方法来预测WWTP的能耗。实验了三种不同类型的模型，特别是长短期记忆网络（LSTM），门控循环单元（GRU）和一维卷积神经网络（CNN）。评估了单变量和多变量设置以及处理异常值的不同方法。通过基于CNN的模型获得了可喜的预测结果，与LSTM和GRU相比，该差异具有统计学意义，而在多变量设置下，最佳模型的总体误差约为630 kWh。最后，为了克服污水处理厂中数据短缺的问题，实施了转移学习过程，当使用预训练的单变量CNN模型时，可实现令人鼓舞的结果，总误差降低到325 kWh。