ISQ, R.Prof.Dr. Cavaco Silva, 33, Taguspark Oeiras 274-12 Porto Salvo; PORTUGAL BEST FAÇADE Energy Benchmarking of Double-Skin Façade Buildings Rogério Duarte Mário de Matos
INTRODUCTION Double-skin façade (DSF) is a building related technology of high-rise glazed office buildings, which has gained significant acceptance among architects and promoters. Actual energy performance of DSF buildings is still lacking a more accurate evaluation. Main objective of this work is: To assess the circumstances for which the DSF technology has energetic advantages Based on existent DSF buildings to discover the best energy practices of this technology For this purpose a benchmarking analysis tool was considered to examine DSF buildings: Work developed at ISQ deals specifically with analysis and interpretation of energy performance data ENERGY RELATED BENCHMARKS ISQ: Instituto de Soldadura & Qualidade 22.11.26:2
THE BENCHMARKING DATA Target group directly adressed by BestFaçade (27+3 DSF buildings) Number of buildings 5 4 3 2 1 Sw eden Austria Belgium France Germany Greece Portugal Data collection tools: Questionnaires Interviews Technical visits Additional documentation research Sample quality Despite of the data collection effort... Detailed main consumptions Climat+Elect consumption data 2 9 'stand-by' estimated 11 Buildings Sample by main climatic region as classified in WP1 report Climat consumption data Returned questionnaires Initial sample 11 24 forecast measured data 2; 18% 4; 36% 5; 46% nordic moderate mediterranean 5 1 15 2 25 3 Number of DSF Buildings ISQ: Instituto de Soldadura & Qualidade 22.11.26:3
EXAMPLE OF ENERGY BENCHMARKS tatistical analysis of energy consumption related to one or more variables Cooling supply energy system 1/11 Cooling space HVAC system Solar shading 5/11 5/11 3/11 4/11 4/11 9/11 2/11 district cooling electricity (Other) overhead radiation cooling hot air cooling (Other) venetian blind canvas screens Heating supply energy system 2/11 1/11 6/11 2/11 Heating space HVAC system 4/11 5/11 2/11 8/11 Daylight control systems 3/11 1/11 1/11 1/11 district heating electricity gas/fuel (Other) radiator hot air heating (Other) without manual semi-auto automatic AIM: allow a meaningful comparison between DSF buildings, with different typologies, subject to different environments, management conditions, etc ISQ: Instituto de Soldadura & Qualidade 22.11.26:4
ENERGY PERFORMANCE INDICATORS Buildings energy needs are ( without difficulty ) obtained from energy billing accounting procedures. [kwh/m2a] ANNUAL ENERGY DELIVERED PER m 2 USEFUL PAVEMENT AREA [kwh/m 2 a] normalizes energy consumption by building size and is usually called Energy Efficiency Indicator [kwh/m 2a] 6 5 4 3 2 1 55 5 45 4 35 3 25 2 15 1 5 1 2 3 4 5 1 2 21 23 48 49 Buildings Heating Cooling Energy consumption total climatization 1 2 3 4 5 1 2 21 23 48 49 Buildings Good and poor performers are easily identified! Statistical parameters 25 2 15 1 5 EEI: Energy Efficiency Indicator V C A D B G Q E R T W DSF Building code Average EEI = 123 kwh/m 2.a Mode = 112 kwh/m 2.a Median (percentile 5) = 112 kwh/m 2.a Percentile 95 = 221 kwh/m 2.a Percentile 5 = 51 kwh/m 2.a ISQ: Instituto de Soldadura & Qualidade 22.11.26:5
CLIMATE NORMALIZATION METHOD Because buildings in different cities around Europe are to be compared, it is imperative to normalize energy performance by the severity of the climate. Normalization procedure was based in the heating and cooling degree-day method, by making use of TAPM generated climatic data for each city in which there was a DSF building Main advantage of the method chosen: On a coherent basis is possible to compare buildings subjected to different climate Main disadvantage of the method chosen: Solar radiation gains are not well thought-out in the degree-day method It was used a 21ºC base indoor temperature in the calculations for heating and cooling seasons It was assumed that heating or cooling could occur during the entire year Energy consumption data considered in this work was obtained for different years, so in some cases a normalization procedure was used also for annual city climatic variations. ISQ: Instituto de Soldadura & Qualidade 22.11.26:6
CLIMATE NORMALIZATION EEI RESULTS EEI: Energy Efficiency Indicator normalized 6 55 5 45 4 35 3 25 2 15 1 5 BEST PRACTICES D C A B E V G Q R T W [kwh/m 2.a] 25 2 15 1 5 EEI: Energy Efficiency Indicator V C A D B G Q E R T W DSF Building code From a previous slide Statistical parameters DSF Building code Average EEI = 268 kwh/m 2.a Median (percentile 5) = 221 kwh/m 2.a Percentile 95 = 575 kwh/m 2.a Percentile 5 = 55 kwh/m 2.a Good performers are still the good performers! One of the three best performers (R,T and W) is a experimental test facility. V,E and D changed their rate more than one position. These buildings are each located in the three different WP1 climatic regions. ISQ: Instituto de Soldadura & Qualidade 22.11.26:7
ENERGY NEEDS FOR HEATING AND COOLING Due to lack of data and as reference sample could be considered small, it was decided to include single skin façade (SSF) office buildings in the statistical analysis. Forecast was then: data of 58 buildings!... however only 17 have supplied annual energy consumption heating and cooling, which is the minimum data for an energy performance benchmark related analysis Energy Consumption Index [kwh/m2a] 25 2 15 1 5 heating cooling heating cooling 1 2 3 4 5 DSF buildings SSF buildings Energy Consumption Index Climate Normalized [kwh/m2a] 6 5 4 3 2 1 heating cooling 1 2 3 DSF buildings Tendencies are as shown: Actual buildings have both heating and cooling consumptions with minimum values between 25 and 5kWh/m 2.a For heating, maximum values are between 15 and 175kWh/m 2.a For cooling, maximum values are between 75 and 1kWh/m 2.a Europe consumes more heating in actual buildings than cooling energy, but if taking in account the climate region, the scenario is different specific local DSF solutions ISQ: Instituto de Soldadura & Qualidade 22.11.26:8
INTRODUCTION OF SSF SAMPLE WITH THE DSF SAMPLE Heating consumption index 25 2 15 1 5 1 11 21 31 41 51 61 Sample with DSF (dark painted) and SSF together Cooling consumption index 25 2 15 1 5 1 11 21 31 41 51 61 Sample with DSF (dark painted) and SSF together ISQ: Instituto de Soldadura & Qualidade 22.11.26:9
INTRODUCTION OF SSF SAMPLE WITH THE DSF SAMPLE Ventilation consumption index Lighting consumption index 25 25 225 225 2 2 175 175 15 125 1 75 5 25 1 11 21 31 41 51 61 Sample with DSF (dark painted) and SSF together 15 125 1 75 5 25 1 11 21 31 41 51 61 Sample with DSF (dark painted) and SSF together Electricity consumption index 48 25 225 2 175 15 125 1 75 5 25 1 11 21 31 41 51 61 Sample with DSF (dark painted) and SSF together ISQ: Instituto de Soldadura & Qualidade 22.11.26:1
COMPARISON WITH EUROPEAN BENCHMARKS CLUSTER ANALYSIS CLIMATIZATION ENERGY CONSUMPTION INDEX [kwh/m2a] 25 2 Cooling [kwh/m2a] 15 1 total 155kWhe/m2a (EPBD - PT:new Banks) total 121kWhe/m2a (EPBD - PT:new Offices) SSF AC Benchmark good Greece total 1kWh/m2a (EPBD - SE) SSF AC Benchmark typ Greece DSF - Moderate 5 DSF - Moderate SSF AC Benchmark typ UK-prestige DSF - Moderate DSF - Moderate SSF AC Benchmark good UK-prestige SSF AC Benchmark typ UK DSF - Moderate SSF AC Benchmark good UK 5 1 15 2 25 Heating [kwh/m2a] ISQ: Instituto de Soldadura & Qualidade 22.11.26:11
Energy Related Clustering Analysis of DSF Buildings ENERGY CLUSTERS OF CLIMATIZATION CONSUMPTION CLIMATIZATION ENERGY CONSUMPTION INDEX [kwh/m2a] 25 Cooling [kwh/m2a] 2 15 1 total 155kWhe/ m2a (EPBD - PT:new Banks) 5 total 121kWhe/ m2a (EPBD - PT:new Of f ices) W AD total 1kWh/ m2a (EPBD - SE) AB E SSF AC Benchmar k good Gr eece G T R Q SSF AC Benchmar k typ Gr eece B AE SSF AC Benchmar k good UK-pr estige SSF AC Benchmar k good UK SSF AC Benchmar k typ UK SSF AC Benchmar k typ UK-pr esti ge 5 1 15 2 25 A V C DSF - Moderate Heating [kwh/m2a] ISQ: Instituto de Soldadura & Qualidade 22.11.26:12
Conclusions & Future Work It was very difficult to obtain sufficient data to perform a more detailed analysis, as we hoped and expected at beginning of our work There are DSFs that perform better and DSFs that perform worse than SSF With the existing data a statistical treatment was questionable so cluster analysis was used Cluster analysis identified regional trends Future work will help identify within the clusters the common characteristics ISQ: Instituto de Soldadura & Qualidade 22.11.26:13