Accurate forecasting of heat consumption plays an important role in effective management of heating utility, such as, unit commitment, short term maintenance, network’s power flow optimization, etc. Inaccurate forecasting of consumption may lead to increase in operating cost. Over-forecasting leads to unnecessary reserved cost and excess supply. Under forecasted loads result in high expenditures in the peaking unit. Hence it is important for a utility, such as a district heating network to forecast Heat consumption patterns could vary depending upon the external temperature and day of usage, such as holiday or weekend.
The consumption pattern also varies based upon consumer type, operational reasons and consumer activities. This gives rise to the motivation of capturing the load profile feature of a certain consumer to tackle model variance when using ML based forecasters. This means, the forecasting model should capture information (feature) of a user’s usage/consumption pattern from three dimensions: Time, Frequency and Magnitude. The time series of heat consumption consist of several discontinuities or abrupt jumps which may carry important information. Therefore a highly accurate prediction of heat consumption of an end-user could be yielded by incorporating the discontinuities through the approximation of functional non-linearity. Moreover, the consumption pattern also varies based upon consumer type, operational reasons and consumer activities. Therefore in order to ensure the generalizability of the model across different types of end-users the forecasting model should capture features or information about the consumption pattern of different types of end-users. This project also investigates the research question of generalizability of the model through the evaluation of performance quantitatively and qualitatively. The thesis consists of the following aspects:
- Literature review of heat consumption classification in district heating network.
- Analysis and understanding of the heat consumption data from a utility.
- Development of a sophisticated heat consumption forecaster.
- Comprehensive evaluation of the forecasting performance by comparing with existing forecasting models.
References:
[1] Y. Zhao, Y. Shen, Y. Zhu and J. Yao, “Forecasting Wavelet Transformed Time Series with Attentive Neural Networks,” 2018 IEEE International Conference on Data Mining (ICDM), 2018, pp. 1452-1457, doi: 10.1109/ICDM.2018.00201.
[2] Chatterjee, Satyaki and Bayer, Siming and Maier, Andreas K “Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model”, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021, https://www.climatechange.ai/papers/neurips2021/42
[3] Kováč, Szabolcs and Micha’čonok, German and Halenár, Igor and Važan, Pavel,” Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network”, Special Issue “Artificial Intelligence in the Energy Industry”, https://www.mdpi.com/1996-1073/14/6/1545