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Institute
Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms
(2021)
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.
This paper presents a methodology to estimate the biomass energy potential and its associated uncertainty at a country level when quality and availability of data are limited. The current biomass energy potential in Colombia is assessed following the proposed methodology and results are compared to existing assessment studies.
The proposed methodology is a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential. The paper also includes a proposed approach to quantify uncertainty combining a probabilistic propagation of uncertainty, a sensitivity analysis and a set of disaggregated sub-models to estimate reliability of predictions and reduce the associated uncertainty. Results predict a theoretical energy potential of 0.744 EJ and a technical potential of 0.059 EJ in 2010, which might account for 1.2% of the annual primary energy production (4.93 EJ).
Hintergrund: Fluktuierende erneuerbare Energien erfordern flexible Kraftwerke zum Lastausgleich
Projektziele: Erhöhung der Flexibilität kommunaler Kraft-Wärme-Kopplungsanlagen (KWK) durch:
- Optimierte Anlagendynamik
- Wärmespeicherung
- Nutzung von Power-to-Heat
- Einbindung von Strommärkten
Vorgehensweise:
- Instationäre Prozesssimulation der Anlagen
- Nicht-lineare Optimierung der Anlageneinsatzplanung
- Demonstration durch Einbindung in die Leitstandstechnik
- Erstellung eines Betreiberleitfadens