Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network

Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach:

• An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network.

• A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity.

• Predictions for each time point are based on data from a wider, 600-minute, time window.

We evaluate model performance on a dataset with 10 minute granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods.