A simplification of weather data to evaluate daily and monthly energy needs of residential buildings

Hourly, daily, monthly and annual heating and cooling requirements of a residential building located in Ottawa, Ontario, Canada were estimated, employing ENERPASS as the energy simulation tool, and performing hour-by-hour energy analysis. The following weather data were employed:

1. (i) Ten years (1967–1976) of weather data. The ten-year average of the results is identified as TYA.

2. (ii) A typical meteorological year (TMY) generated using the same ten years of data.

3. (iii) Two different hourly ambient air temperature distributions (T1 and T2) for a typical day in each month. The solar radiation on each surface was estimated using the mean monthly clearness index.

The house use patterns, including heat generation and the thermostat setting, were taken the same when using TYA, TMY, T1 or T2. The analysis was carried out for the house as it is (well insulated and airtight), and for two modifications: one with larger infiltration rate and lower wall thermal resistance, and the other with larger south-facing window area and using super-windows. The results of this study show that the long-range hourly, daily, monthly and annual heating and cooling requirements of a residential building located in a cold climate can be predicted by employing mean daily maximum and minimum temperatures and the mean monthly clearness index for each month. This amounts to substantial savings in computational costs, in either using many years of weather data or generating a TMY for the site. For locations lacking detailed hourly weather data, the use of data and the procedure outlined in this study may be employed to predict the long-range thermal performance of simple residential buildings.