London’s urban heat island increases the mean air temperature which affects the demand for heating and cooling buildings. Measured air temperature data have been used as input to a building energy simulation computer program to assess the heating and cooling load of a typical air-conditioned office building positioned at 24 different locations within the London Heat Island. It is found that the urban cooling load is up to 25% higher than the rural load over the year, and the annual heating load is reduced by 22%. The effect of raised temperature and urban context are assessed separately, and the sensitivity of the net impact to the internal gains in a building is determined. For the estimation of peak cooling demand, we propose hourly temperature corrections based on radial distance from London’s centre to be applied to standard published temperatures for the region. For more detailed investigations over the cooling season a range of models is available. These are reviewed in this paper and we describe preliminary results of an Artificial Neural Network (ANN) model that predicts location specific hourly temperatures for London, taking into account radial distance from central London, hourly air temperature measured at the meteorological station and associated synoptic weather data.