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 min 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.

The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes.

The IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.