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

ABSTRACT: 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 ta

SUBMITTER: Berliner N 

PROVIDER: S-EPMC8374346 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature