Represent senor data of district heating network using contrastive learning

Type: MA thesis

Status: running

Supervisors: Siming Bayer, Adithya Ramachandran, Andreas Maier

According to the European Commissions, heating and cooling in buildings and industry take up half of the total EU energy consumption [1]. One of the state-of-the-art thermal energy supply infrastructures is the district heating network, which is widespread in middle and north Europe. Thus, a robust and efficient management of energy supply trough out district heating networks has significant impact on reducing the greenhouse gas emission. In the last decades, smart sensors, such as pressure or consumption sensors, have been utilized to monitor the condition of the networks, therefore providing a solid basis for further data-driven approaches addressing various tasks, e.g., heat load prediction, automated anomaly detection etc.

With the latest advances of machine learning and deep learning, data-driven approaches have gained more importance and applicability for improving the efficiency of district heating networks. Although many works have been proposed for heat load prediction [2, 3] or anomaly detection [4] to date, a fundamental challenge remains the learning of universal representation for time series. Recently, contrastive learning paradigm has been adapted for the learning tasks with time series [5]. However, the proposed works haven´t been validated with real-world sensor data of district heating network. In this work, we are aiming at finding appropriate representation using contrastive learning for real-world sensor data acquired from district heating network. The thesis should consist of the following aspects:

  1. Literature review of contrastive learning for time series
  2. Analysis and understanding of real-world sensor data
  3. Develop and implement contrastive learning framework for district heating network
  4. Evaluate the proposed method(s)

[1] European Commission, Heating and cooling. 2018

[2] Xue et al., District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model. Energies 2019, 12, 2122.

[3] Chatterjee et al., Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, Dec. 2021.

[4] F. Zhang and H. Fleyeh, “Anomaly Detection of Heat Energy Usage in District Heating Substations Using LSTM based Variational Autoencoder Combined with Physical Model,” 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020, pp. 153-158, doi: 10.1109/ICIEA48937.2020.9248108.

[5] Yue et al., TS2Vec: Towards Universal Representation of Time Series. arXiv:2106.10466. Feb.2022.