Development of an England-wide indoor overheating and air pollution model using artificial neural networks

<p>With the UK climate projected to warm in future decades, there is an increased research focus on the risks of indoor overheating. Energy-efficient building adaptations may modify a buildings risk of overheating and the infiltration of air pollution from outdoor sources. This paper presents the development of a national model of indoor overheating and air pollution, capable of modelling the existing and future building stocks, along with changes to the climate, outdoor air pollution levels, and occupant behaviour. The model presented is based on a large number of EnergyPlus simulations run in parallel. A metamodelling approach is used to create a model that estimates the indoor overheating and air pollution risks for the English housing stock. The performance of neural networks (NNs) is compared to a support vector regression (SVR) algorithm when forming the metamodel. NNs are shown to give almost a 50% better overall performance than SVR.</p

Fig 4 -

A) Reconstruction of the time-series of Campylobacter cases in England and Wales. B) Seasonal patterns for daily Campylobacter cases averaged over 19 years. The shaded area represents the 25% and F quantiles. Weather variables are maximum air temperature, relative humidity and day-length. C-D) Scatter plot and map comparing the reported and predicted daily number of campylobacteriosis per catchment area averaged over the entire 19 years. In D) the red circles represent the reported cases while the blue squares the predictions. Weather variables averaged over the past 14 days. Map reproduced in R [45] using shapefiles availalbe at [46].</p

Campylobacteriosis cases per 1, 000, 000 per day conditioned to maximum air temperature, relative humidity and day-length.

As the day-length depends on the time of the year (as well as latitude), each panel broadly correspond to (A) last week of October—middle of February, (B) middle of February—first week of April and middle of September- last week of October (C) first week of April—second-half of May and second-half of July—middle of September (D) second-half of May—second-half of of July 22. Data were averaged over the past 14 days. The shaded area shows the 95% confidence intervals for the Poisson means using the normal approximation (i.e. . Data divided by quantiles.</p

Illustrative description of the method. Created with BioRender.com.

Illustrative description of the method. Created with BioRender.com.</p

Prediction of seasonal patterns for daily <i>Campylobacter</i> cases as done in Fig 4 for the situation when 2 variables are constant (Weather variables averaged over the past 14 days).

A) Constant relative humidity 76% and day-length 15 hours. B) Constant maximum air temperature 20°C and day-length 15 hours. C) Constant maximum air temperature 20°C and relative humidity 76%. D-E-F) Patterns for daily 14-days rolling mean for maximum air temperature, relative humidity and day-length averaged over 19 years. The shaded area represents the 25% and 75% quantiles. G-H-I) Conditional incidence vs the variable weather factors for the situation corresponding to A) B) and C) respectively.</p

Campylobacteriosis cases per 1, 000, 000 per day <i>vs</i> (A) maximum air temperature, (B) minimum air temperature (C) relative humidity (D) rainfall (E) mean wind speed and (F) day-length.

Data were averaged over the past number of days represented by the time-lag. The shaded area shows the 95% confidence intervals for the Poisson means using the normal approximation (i.e. . Data divided by quantiles.</p

The file contains the following section: Regional structure of UK Health Security Agency, diagnostic laboratories and their catchment areas.

Removing Reporting Delays and the effect of Incubation Period. Correlations among the weather variables and their distributions. Validation with Agent Based Models. Patterns in conditional incidence according to different weather variables (two weather factors simultaneously). Different Ways to Visualize conditional incidence (three weather factors simultaneously). Patterns in conditional incidence according to different weather variables (four weather factors simultaneously). Patterns in conditional incidence according to maximum air temperature and relative humidity for different periods of the year. Incidence of campylobacteriosis cases when the weather variables are averaged over different time-lags (three weather factors simultaneously). Seasonal patterns for daily Campylobacter cases using only one predictor. Seasonal patterns for daily Campylobacter cases using only two predictors. Predictions using rainfall, instead of relative humidity, as predictor. (PDF)</p