ABSTRACT
In this chapter, we present useful and widely used simplified analysis methods that allow annual (or seasonal or monthly) primary energy (either heating or cooling) needs of a building to be determined without having to resort to a full-fledged hour-by-hour simulation over the year. The degree-day method relies on a single-weather datum to characterize the severity of the weather of the location in question. The annual (or seasonal or monthly) energy consumption is then directly proportional to the corresponding degree-day value. It is applicable to residential and light-commercial buildings where energy use is primarily driven by building loads and that normally have simple HVAC equipment whose efficiency can be characterized by a single constant value. An extension of the basic degree-day method is the bin method that gets its name from the way the weather data are assembled. Here, the weather is broken up in outdoor dry-bulb temperature bins and the cumulative number of hours in each bin is determined for the location in question from long-term climatic records. Then, calculations for each bin are done from which the annual or seasonal energy use can be estimated. These methods are widely used for preliminary sensitivity analyses of design options and also by engineering service companies contemplating energy efficiency upgrades in existing buildings. Hence, advantages as well as limitations of these methods are described in this chapter along with numerous solved examples to enhance comprehension. In the last section, we introduce the concept of inverse modeling and provide an overview of single-variate and multivariate steady-state models along with dynamic and hybrid modeling approaches. This modeling approach is being increasingly used when the building already exists and one wishes to improve its operational energy efficiency by making use of its actual energy consumption monitored at different time scales: monthly, daily, hourly, and subhourly. Inverse models are useful for verifying energy efficiency measures that have been implemented, for energy management, for fault detection, and for supervisory control, to name a few applications. We close with a brief description and summary of the principal methods.
