Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms

DOI

10.2991/ahe.k.210202.001How to use a DOI?

Keywords

district heating and cooling, load forecast, machine learning, stacking

Abstract

For district heating, heat demand forecasting is playing a key role for an optimised power plant dispatch. Machine Learning can help to significantly improve forecasts of thermal loads. The prediction quality of neural networks is higher than that of decision trees in most cases. However, compared to decision trees neural networks have weaknesses when extrapolating outside known ranges. This work presents a novel method called “Deep DHC” (Deep Learning for District Heating and Cooling), which combines these two approaches in order to benefit from strengths of both methods. On the one hand, the novel approach uses conventional decision tree based regression algorithms such as the AdaBoost and Random Forest, as well as artificial neural networks. In addition to common feed forward neural networks (FNN), a deep learning network structure, which consists of long short-term memory (LSTM) cells, is used for the first time. The LSTM method has already proven to be very powerful in modern speech recognition. In order to achieve best possible heat demand forecasts, the aforementioned methods for load forecasting are combined and weighted by an additional machine learning method. Results show that it is possible to achieve a further improvement in forecasting quality for district heating loads by purposefully combining individual forecasting methods. Hence, mean and absolute deviations are significantly reduced in comparison to the individual methods.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Till Faber
AU  - Matthias Finkenrath
PY  - 2021
DA  - 2021/02/04
TI  - Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms
BT  - Proceedings of the 14th International Renewable Energy Storage Conference 2020 (IRES 2020)
PB  - Atlantis Press
SP  - 1
EP  - 4
SN  - 2589-4943
UR  - https://doi.org/10.2991/ahe.k.210202.001
DO  - 10.2991/ahe.k.210202.001
ID  - Faber2021
ER  -