Heat demand prediction: A real-life data model vs simulated data model comparison

Abstract

In the recent years machine learning algorithms have developed further and various applications are taking advantage of this advancement. Modern machine learning is now used in district heating for more precise and realistic heat demand prediction. Machine learning methods like Artificial Neural Network (ANN), Linear Regression (LR), and Decision Tree (DT) are commonly adopted in heat demand prediction to produce more accurate results. This research paper compares the performance of several machine learning methods on different datasets generated by the combination of simulations and real-life data collected from a local district heating site in Nottingham. The result shows that Linear Regression generates better prediction than Artificial Neural Network and Decision Tree, for dataset generated using simulator, whereas Decision Tree performs best for real-life data.

Keywords

• Heat demand
• Prediction
• Machine learning
• District heating
• Data models