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

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