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A new degree-day model for estimating energy consumption in buildings has been developed that removes the need for correction factors and allows the user more control over the inputs. The proposed method incorporates a model for determining mean internal temperatures of buildings. The model has been tested for accuracy and sensitivity to input variations. It has also been possible to quantify the uncertainty in the model, which allows the user to determine the usefulness of a result to a particular application.

A formal method has been developed for quantifying the uncertainties in degree-day-based energy estimates in buildings. Different interpretations of base temperature as a means of describing the building energy balance were investigated, with the result of an improved degree-day model that does not require the use of correction factors. This model has been tested using high-quality data from building simulations, and the outputs have been subjected to statistical uncertainty analysis. The results show that degree-day uncertainties diminish with longer time frames. The uncertainties also impact on regression techniques used in monitoring and targeting of building performance, and these uncertainties may exceed typical targets for energy efficiency improvements.

The uncertainties associated with calculating degrees-days from traditional formulae and other approximate methods were examined. Comparisons were based on calculations using hourly temperature records. A definition of uncertainty for monthly degree-days and some typical errors in annual values are presented. The ranges of uncertainty are small for published degree-days, but could be significant for a small percentage of occasions. If the methods are used for base temperatures lower than the current standard then the uncertainties increase. Also shown are the effects of these errors on the regression analysis often used in building energy monitoring.

The main findings of a detailed literature search on solar-assisted heat pumps (SAHP) are presented. The various system types are described and the relevant performance criteria by which systems can be assessed are defined. Where possible different system performances are compared and specific conclusions drawn. While most work in the literature has focused on series systems there is no evidence that these can compete with parallel systems, either in non-purchased energy delivery or, more significantly, in cost. Efforts to improve control strategies for series systems are unable to achieve better performance. Technology is sufficiently advanced to exploit the best of these systems if the economic climate becomes favourable. Some suggestions are advanced as to where future research effort can make a useful contribution.