Evaluation of a Machine Learning Approach To Heat Prediction

This is a report about machine learning in the field of computer science. The problem handled is prediction of energy consumption in district heating systems. Prediction of energy consumption in district heating systems is a delicate problem because of the social behaviours, weather and distribution time that has to be accounted for. One algorithm is introduced and three different experiments are made to determine if the algorithm is useful. The results from the experiments were good. This report differs in approach to the problem then other reports found in this field. The difference is that this report tries to handle social behaviours and looks at a decentralized view of the problem instead of centralized.
Contents
1. Introduction
2. Background
2.1 District Heating Systems
2.2 Forecasting
2.3 Machine Learning
2.4 Weighted K-Nearest Neighbour
2.5 Genetic Algorithms
2.6 Neural Networks
3. Hypothesis
3.1 Research Questions
4. Goals
5. Delimitations
6. Heat Prediction in District Heating Systems
6.1 Problem description
6.2 The Simulator
6.3 Simulated Experiments
6.4 Experiment Results
6.5 Additional Experiment
7. Conclusions
8. Discussion
9. Acknowledgement
10. References
Appendix
Author: Kenny Svensson
Source: Blekinge Institute of Technology

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