Model Simplification Techniques

The methods described below rely on some form of model simplification to reduce simulation complexity or calibration requirements.

2.5.3.1 Base-Case Modelling

This process relies on the key concept of a detailed base-load energy consumption determination (Yoon and Lee 1999; Yoon et al. 2003) using the swing-season base load analysis

recommendation by Lyberg (1987), where the term ‘base load’ refers to the minimum (or weather-independent) electricity or gas consumption. The swing-season calibration then fine- tunes simulation inputs when heating and cooling loads are minimal and building behaviour is dominated by internal loads. This provides a unique way to calibrate non-weather dependent data. Yoon et al. (2003) illustrate how this step-wise base-case modelling and swing-case calibration has been applied to a 83,212m2 _{commercial building in Seoul, Korea. The final} simulation gave an annual NMBE of 2.3% and CV (RMSE) of 3.6% for monthly data.

2.5.3.2 Model Parameter Estimation

The process of macro-parameter estimation refers to the process of deducing overall values for aggregated individual building parameters using non-intrusive monitored data, as opposed to intrusive tests described in section 2.5.1.4. Reddy et al. (1999) propose the use of an inverse method for estimating building and ventilation parameters through non-intrusive monitoring of heating and cooling energy use in large commercial buildings. As discussed in a later review paper by Reddy (2006), the procedure involves deducing the loads of an ideal one-zone building from the monitored data and then, in the framework of a mechanistic macro-model, using a multistep linear regression approach to determine the regression coefficients (along with their standard errors), which can finally be translated into estimates of the physical parameters. This procedure is applied to two different building geometries at two different climatic locations, to estimate six physical parameter values, including the overall building heat loss coefficient. The approach has been found to yield very accurate results (regression R2 _{coefficients of 0.97-0.99),} particularly when combined with daily data over an entire year.

2.5.3.3 Parameter Reduction (Day-Typing and Zone-Typing)

The process of parameter reduction or simplification relies on the statistical characterisation of complex inputs in order to reduce the number of inputs in a model. One approach which has been used extensively is day-typing, in which building energy use is characterised on a daily profile, rather than on an hourly basis. This approach allows for the definition of typical days (e.g. weekdays, weekends, and holidays) which can be used to characterise building energy use, thus condensing a large quantity of complex measured building data into relatively few input points or schedules.

Kaplan et al. (1990; 1990) use day-typing to group days with reasonable uniform non-HVAC load shapes. Zone-Typing (i.e. grouping similar zones) is used to further apply these day-types across multiple zones. Bronson et al. (1992) uses day-typing routines (for occupancy and equipment scheduling) to calibrate a DOE-2 simulation model. Hadley (1993) uses a

combination of principal component analysis and cluster analysis to identify distinctive weather day types (which represent repeatable weather conditions that typically occur at each site) from one year of National Weather Service (NWS) station data. HVAC system energy consumption data for each day are then grouped by these weather day types, and daily total and hourly load profiles were developed for each day type.

Raftery et al. (2011; 2011) incorporate zone-typing to separate thermal zones in such a way as to minimise inaccuracies incurred by representing multiple actual thermal zones in a building with a single large zone in the model. This is achieved by assigning thermal zones in the model based on four major criteria (see Figure 2-7): (1) space function, (2) position relative to exterior, (3) available measured data, and (4) space conditioning method.

Figure 2-7: Zone Typing (Raftery, Keane, O’Donnell, et al. 2011)

2.5.3.4 Data disaggregation

Disaggregation is the splitting up of the total building energy consumption into its component parts. There are a number of reasons as to why this is done, i.e., to focus on specific energy flows and identify areas for retrofit and conservation. Lyberg (1987) proposes data disaggregation as part of a staged audit process as a means of focussing attention on high-importance areas. This can help limit subsequent auditing to the areas where the most productive retrofits could be

carried out. This step will directly assist in the identification of energy-conservation opportunities (ECO’s).

Akbari (1988; 1995) developed an algorithm to disaggregate short-interval (hourly) whole building electrical load into major end-uses. The End-Use Disaggregation (EDA) algorithm utilises statistical characteristics of measured hourly, whole-building load and its inferred dependence on temperature to produce hourly load profiles for air-conditioning, lighting, fans, pumps and miscellaneous loads. Regression models are developed for each hour of the day for major day types (see 2.5.3.3) between measured building energy use and outdoor dry-bulb temperature. Since the temperature dependency of the building may change with season, the author suggests using two season specific (summer and winter) sets of temperature regression coefficients. The regression constant for these models are assumed to provide an indication of the weather-independent energy use, while the slope represents weather-dependent behaviour. Since the regression models provide no information about the breakdown of the temperature- independent load, it is simply pro-rated against loads predicted by simulation as well as on-site measurements. The approach is applied to numerous retail and commercial facilities. (Akbari et al. 1988; Akbari 1995; Akbari and Konopacki 1998). The authors conclude that this is a useful approach for buildings in which the whole-building temperature dependent load is primarily due to the HVAC system (i.e. only the HVAC load is sensitive to outdoor temperature). This

assumption may be applied to large offices and commercial buildings, but not to buildings characterised by non-HVAC end-uses such as refrigeration (which is weather dependent).