STORM wins Award for Research Excellence at DHC2016 conference

Paper on STORM controller wins Award in Research Excellence at DHC2016 conference in Seoul, South Korea in September 2016.

In the beginning of September 2016, the STORM consortium was present at the 15th International Symposium on District Heating and Cooling in Seoul, South Korea. Dirk Vanhoudt (EnergyVille/VITO) gave a general presentation of the STORM project and the latest status. Moreover, Christian Johansson (NODA) presented a research paper on the first results of the energy demand forecasting algorithm, which is part of the STORM controller. We are proud to announce that this paper won the ‘Award for Research Excellence in District Heating and Cooling’ by the International Energy Agency’s District Heating and Cooling Programme (IEA DHC) and the Korean District Heating & Cooling Association (KDHC).

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 Dirk Vanhoudt presents the STORM project at DHC2016

What is the paper about?

The awarded paper describes a number of self-learning algorithms to forecast the energy consumption of a heating or cooling network for the next 24 hours. Indeed, this forecast is of high importance for the STORM controller because it identifies the moments where the controller must intervene.

The forecast is based on two types of machine learning algorithms to forecast the future consumption in the network, namely Extra-Trees Regressors (ETR) and Extreme Learning Machines (ELM). Both algorithms use the historic consumption data in the network to ‘learn’ the relationship between historical data and the forecasted future consumption. More specifically, the first method uses tree-based algorithms and the second uses a type of neural networks.

These algorithms have already been implemented in the demonstration network in Rottne, Sweden. Hence, the paper presents the results of three months of online heat load forecasting, i.e. January to March 2016. All in all, the algorithms need about three weeks to learn the network behaviour and to be able to produce sufficiently good results. In general, the performance of the ELM algorithms is slightly better than the ELM algorithms. During February, the prediction was best, leading to very good ‘mean absolute percentage errors’ of respectively 7.6 and 6.8%.

Read the whole paper here: Operational demand forecasting in district heating systems using ensembles of machine learning algorithms

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Christian Johansson from NODA receives the award for research excellence on the STORM controller

Next steps

The STORM consortium sees this award as motivation for their future research efforts and to continue improving the controller.