Automating dataflow for a machine learning algorithm

Abstract: Machine learning algorithms can be used to predict the future demand for heat in buildings. This can be used as a decision basis by district heating plants when deciding an appropriate heat output for the plant. This project is based on an existing machine learning model that uses temperature data and the previous heat demand as input data. The model has to be able to make new predictions and display the results continuously in order to be useful for heating plant operators. In this project a program was developed that automatically collects input data, uses this data with the machine learning model and displays the predicted heat demand in a graph. One of the sources for input data does not always provide reliable data and in order to ensure that the program runs continuously and in a robust way, approximations of missing data have to be made. The result is a program that runs continuously but with some constraints on the input data. The input data needs to be able to provide some correct values within the last two days in order for the program run continuously. A comparison between calculated predictions and the actual measured heat demand showed that the predictions were in general higher than the actual values. Some possible causes and solutions were identified but are left for future work.