REGIONAL CLASE III.

Benchmarking plays a key role in the development of energy efficiency in buildings. Benchmarking is usually used as a practical approach for energy management in both existing and developing buildings to analyse and enhance the efficiency of buildings. Based on the granularity of data, benchmarking methods have been classified into two approaches top-down and bottom-up. The bottom-up approach refers to techniques in which whole-building energy

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determinants are calculated and simulated by the aggregation of detailed data obtained from sub- systems. The bottom-up approach can be classified into two main groups; building physics and end-use [20].

The top-down method is applied as a valuable way to understand how similar buildings (peers) perform. Calculating the energy performance of a building and then comparing the result with the performance of that of its peers indicates the situation of that building among the group. In other words, instead of an absolute value resulting from the bottom-up method, the relative performance shows the quality of building in terms of energy efficiency hence, the index pushes for more improvement and this is a powerful motivator. Descriptive Statistics (DS) and Artificial Neural Network (ANN), Carbon Trust, and CIBSE are examples of the top-down approach. Released in 2008, CIBSE TM46 benchmarking methodology, a top-down method, immediately underpinned the UK’s Display Energy Certificate scheme [15].

The robustness of the top-down method depends on the richness, quantity, and quality of DEC dataset. Acquiring sufficient and useful data were recognised as limitations of the top-down approach particularly in benchmarking of non-domestic buildings [105]. Regarding the top-down approach, the earliest studies were conducted in 1982 [106] in which multiple linear regression models were used to recognize key energy determinants, the approach was developed further by Sharp [48, 49]. These methods formed the basis of US Environment Protection Agency (EPA) Energy Star structure [107]. Accordingly, in 2006 the similar method was applied in Hong Kong [44, 108] for benchmarking of energy efficiency of commercial buildings. Recently in the UK [23] and US [43, 109] the capability of artificial neural network (ANN) for energy benchmarking has been developed as well as the use of the Data Envelopment Analysis (DEA) [42, 110-112]. The top-down method refers to a structure in which an overview is formulated and the main character of the system is taken into account instead of details of the system. In contrast, a bottom- up method relies upon sub-system details and uses them based on a defined methodology. Top- down approach refines further subjects to avoid using detailed information. A bottom-up method, on the other hand, engages with detailed data to develop a more specified overview [105]. In terms of benchmarking, a top-down approach concerns techniques in which benchmarks are calculated based on building-level energy performance. The top-down approach is broadly used in the UK to assess energy benchmarks using descriptive statistics, for instance, 25th _{and 50}th

percentiles of the distribution of energy performance of same type buildings [18, 45, 99, 105, 113]. In addition, similar approaches in Argentina [114] and Greece [106] were also applied for analysing the energy performance of school buildings.

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Hong et al. [105] carried out research combining two top-down techniques e.g., Descriptive Statistics (DS) and Artificial Neural Network (ANN) to explore the purpose of benchmarking energy performance of schools in UK. The researchers used a dataset previously developed by Bruhns et al. [15] and Godoy-Shimizu et al. [16]. The authors also used the results of the research carried out by the Carbon Trust, i.e. 80% of fossil thermal energy is usually consumed for space heating [13], as a baseline case in the analysis. The results were compared with the CIBSE Guide F [115] and CIBSE TM46 [99] benchmarks for School & seasonal public buildings.

The comparison of results with both versions of CIBSE, i.e., Guide F and TM46, showed that schools in the UK in 2010-2012 consumed more electricity and less heat than outlined by CIBSE. According to Table 2.1, primary and secondary schools consumed 121 and 111 kWh/m2 _{of energy}

for heating respectively between February 2010 and June 2012 while CIBSE TM46 predicted 150

kWh/m2 and CIBSE Guide F predicted 164 kWh/m2/yr for Primary schools and 144 kWh/m2/yr for secondary schools. The discrepancy between actual consumption and CIBSE benchmarks is significant[105].

Table 2.1.Energy demand analysis for schools in the UK [105]

The reason for the observed discrepancies refers to the basis of CIBSE TM46:2008 which is mostly obtained from earlier investigations undertaken in 1990s. Since this, equipment particularly IT has grown in use increasing electrical load. The efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems and building material quality such as double-glazing and thermal insulation have been developed and these measurements reduced thermal energy consumption in schools.

The authors [105] argued that evaluating energy efficiency in schools using current CIBSE TM46 benchmarks does not provide useful feedback for building operators because it is not a precise indicator of how schools perform. Therefore, a dynamic dataset as a basis for benchmarking would deliver more accurate data than TM46. Continuous updating as well as refining of the dataset in a top-down model is crucial and this moves toward systematic monitoring/updating and collection of a comprehensive database.

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Furthermore, the authors [105] found that the compactness (the ratio of surface area to volume) is the strongest parameter that affects heat demand followed by year of building construction, floor area, heating degree day and surface exposure. The result of ANN (Artificial Neural Network) assessment presented in Figure 2.4 shows schools with larger perimeter related to the building size and larger external wall area lose more thermal energy than compact buildings so they required higher levels of energy for heating. The occupant density (number of the pupils) has a considerable impact on electricity demand, but its impact on heat demand was only 10%.

Figure 2.4. Effect of building parameters on heat consumption [105]