Statistical analysis
3.7 Research methods
Buildings are made of different energy flows running simultaneously and dynamically. The flows may be lighting, cooling or appliance usage by occupants or machinery. These are complex systems that require a cross-sectional approach.
The rest of this section covers sampling and data collection methods.
The energy audit procedure encompasses not only data collection but also the analysis process. The energy audit is initiated by a walk-through then an analysis exercise. The walk-through is a field visit through a building enabling data collection by observing or measuring objects of interest. The research instruments utilised for the energy auditing each building include:
Self-administered questionnaires
Unstructured non-participant observation
Literature review of national statistics, building regulations, and climatic data The analysis is conducted parametrically using computer simulations in an experimental research design. The analysis conducted is discussed in detail in Section 3.8.
3.7.1 Sampling
Selected sampling is used to capture the data required for the study. The process begins with a list of possible buildings to audit. The list is based on outlined characteristics that fit the scope of the research. These include single use occupancy status, mix-mode cooling, site electricity metering etc.
Twenty-four office building were identified as having the attributes suitable to participate in the research. Seven buildings out of those were omitted as political and security instability made visiting the Nigerian Niger delta unsafe. Another 2 buildings had insufficient data such as log of back-up generator used, and were discarded. In total, 15 buildings were fully audited.
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The buildings are chosen based on the following factors:
Design climates in Nigeria
Building selection
A random sampling method is more suited for a scientific study such as this; however steps were taken to reduce the impact of such a sampling methods as further discussed in section 3.6.2
3.7.1.1 Design climates in Nigeria
Nigeria experiences a variation of climate as one moves from the coast to the northern parts of the country. The climate of a particular location also varies with the time of the year, latitude of the location and landscape (Ajibola, 2001). Nigeria is classified based on indices such as geo-political zones, vegetative belts and design climates. Electricity consumption and thermal comfort are directly affected by climate rather than vegetation or politics therefore the classification based on design climates is adopted.
Komolafe and Agarwal ((1987) in (Ajibola, 2001)), (Ogunsote, 1991) and UNEP/GRID, (2002) have classified Nigerian climates as shown in Table 3-2.
Researchers Table 3-2 Climate classification by different authors
Figure 3-4 shows the climatic classification in Africa by United Nations Environment Programme (UNEP) and Global Resource Information Database (GRID) (2002).
The climatic profiles of three cities namely Lagos, Kaduna and Abuja are shown in Table 3-3 and Figure 3-5. The cities represent the design climate for hot and humid, hot and dry Kaduna
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and a composite of hot and humid and hot and dry respectively. These are major cities within broad climatic classification that house a large number of office buildings suitable for the energy audits. The fieldwork was originally designed to audit buildings in four major cities however, it was not possible to visit Port Harcourt because of political and security issues experienced at the time.
Figure 3-4 Climatic classification in Africa (UNEP/GRID, 2002)
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Figure 3-5 Air temperature profiles for some Nigerian cities (Nigerian meteorological agency)
Monthly temperature (oC) Kaduna Abuja Lagos
Max Min Max Min Max Min
30 23.5 30.9 25.1 29.6 25.5
Ave annual air temperature (oC) 26 28 28
Precipitation (mm) 1192 1221 1538
Humidity (%) 5-95 20-91 34-99
Daylight hours 12 12 12
Wet season 5 months,
May-Sept
Table 3-3 Climatic data of some Nigerian cities 20
Average air temperature (degree celcius)
Kaduna Abuja Lagos Atlantic Ocean
Gulf of Guinea Lagos Port Harcourt
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Measures of comfort such as average, minimum and maximum temperatures are presented in Table 3-3. Others are precipitation and mean daylight hours. Figure 3-6 to Figure 3-8 show average monthly ambient air temperature and two comfort bands. One is calculated from the Neutrality temperature Tn. Neutrality temperature is calculated using the formula shown in Equation 3-1 by de Dear and Brager (1998).
Equation 3-1
Where Tn is Neutrality temperature, and Tave is mean monthly temperature. It should be noted that this equation is is adopted for use as the equation for Neutrality temperature in this research
The other comfort band is proposed by European standards (15251:2007-08, 2007) for mechanically cooled buildings in the summer.
Lagos: There are two wet seasons in Lagos, with the heaviest rains falling from April to July and a weaker rainy season in October and November. There is a brief relatively dry spell in August and September and a longer dry season from December to March. Monthly rainfall between May and July averages over 300mm, while in August and September it is down to 75 mm and in January as low as 35 mm. The main dry season is accompanied by harmattan winds between December and early February. The average temperature in January is 28.5°C and for July it is 25.5°C as shown in Figure 3-6. On average the hottest month is February with a mean temperature of 29.6°C; while July is the coolest month. The average annual temperature is 28oC. Average annual precipitations is 1538mm, and mean daylight hours is 12hrs.
Kaduna: There are two marked seasons in Kaduna; the dry, windy harmattan which is a northeast trade wind, characterized by dust, intensified coldness and dryness and the wet seasons. On the average, the city experiences a rainy season from May to September. Night temperatures in the cooler months may dip to 10oC and day temperatures in hot months rise to 31.6oC. The average annual temperature is 26oC and average annual precipitation is 1192mm. Mean daylight hours is 12hrs. Monthly average temperatures are shown in Figure 3-7. Also shown are thermal comfort bands based on the neutrality temperature and EN15251 (Olesen, 2010) comfort conditions.
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Figure 3-6 Air temperature profiles for Lagos (Nigerian meteorological agency) and thermal comfort band based on the neutrality temperature and EN15251
Figure 3-7 Air temperature profiles for Kaduna (Nigerian meteorological agency) and thermal comfort band based on the neutrality temperature and EN15251
22 24 26 28 30 32
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ambient temperature (oC)
Lagos
Average air temperature EN15251 comfort
22 24 26 28 30 32
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ambient temperature (oC)
Kaduna
Average air temperature
Tn comfort
EN15251 comfort Tn Comfort
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Figure 3-8 Air temperature profiles for Abuja (Nigerian meteorological agency) and thermal comfort band based on the neutrality temperature and EN15251
Abuja: Abuja experiences three weather conditions annually. This includes; a warm, humid wet season beginning from April and ends in October; and a dry season from November to March. In between the two, there is a brief interlude of harmattan. Daytime temperatures reach as high as 30°C and night time lows can dip to 12°C. The high altitudes and
undulating terrain of the Abuja act as a moderating influence on the weather of the territory.
The average annual temperature is 28oCand monthly average air temperatures are shown in Figure 3-8. Average annual precipitation is 1221mm and average daylight hours are 12hrs.
Out of the three cities studied, Abuja, which experiences a composite hot and dry and hot and humid climates is used to test PCM performance. Abuja is chosen because it
experiences both hot and dry and hot and humid climates. This type of composite climate is representative of a significant part of Nigeria. Abuja as the capital city also has a large amount of purpose built and single-use office buildings suitable for the test. This is further discussed in Chapters 5 and 6
3.7.1.2 Building selection
Twenty-four buildings were earmarked for energy audits across 4 major cities in Nigeria.
Seven buildings out of the initial 24 were omitted as political and security instability made visiting Port-Harcourt in the Nigerian Niger delta unsafe. The selection of buildings audited is based on the following criteria;
Design climate 22
24 26 28 30 32
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ambient temperature (oC)
Abuja
Average air temperature EN15251 comfort Tn comfort
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Access to the building
HVAC system
Availability of electricity consumption data
Design climate: The design climate has been discussed in Section 3.7.1.1.
Access to the building: Willingness of office buildings’ management to participate in the investigation imposed access as criteria for selection based on security, bureaucracy or privacy. Access into the office buildings was approved only by the head of the organizations, which then referred the activity to the appropriate department.
HVAC system: The scope of this research covers only mixed mode buildings. These are buildings that use both mechanical and natural means to provide thermal comfort.
However, these buildings are a combination of open and cellular plan configurations.
Natural ventilation during work hours is difficult due to occupancy, peak internal load, casual internal gains, and peak solar radiation coincidence, leading to a reliance on mechanical cooling.
Availability of electricity consumption data: Electricity consumption data for a minimum of a year is crucial to investigate the performance of the building over different seasons.
The performance over three years is optimal to compare within seasons in different yearly cycles. With the paucity of consumption data, only office buildings having adequate records were chosen.
The provision of an on-site electricity meter means the building electricity consumption records from the utility company are more accurate as the meter records are checked by the management against electricity bills.
3.7.2 Data collection
A cross sectional survey method of data collection using a process of energy auditing is used. A cross sectional method of data collection enables the examination of different strains of inquiry affecting building energy consumption. Both primary and secondary methods of data collection are used to examine these different strains as shown in Table 3-4. The table also justifies the use of the methods.
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Data Source Use
Monthly electricity bills Primary quantitative data
Electricity consumption data provided by the state utility based on private electricity metering devices installed.
Alternative power (generator) log books and observation
Primary quantitative data
Back-up electricity consumption data and pictures provided by the building managers based on log usage.
Building characteristics Primary quantitative data
Observation, pictures, diagrams and questionnaire used for accurate modelling.
Aggregate end use Primary quantitative data
Physical inventory recorded for analysis such as lighting, cooling, and appliances loads.
Occupancy and schedules Primary quantitative data
Data recorded in questionnaire for analysis;
comparison of modelled with recorded (calibrating of software) and calculation of savings.
Climatic data Qualitative and quantitative based on literature
Retrieved from Nigerian national archives, synthetic data from Meteonorm. Data required for modelling, simulations and climate description.
National building codes and frameworks
Secondary quantitative data
Data required for analysis and simulations such as minimum fresh air quantity/person
Table 3-4 Justification of collected data 3.7.2.1 Primary sources
Primary data collected include a physical inventory of all electrical equipment, pictures of building components and finishes, observation of building user behaviour and the self-administered questionnaire covering a minimum period of 1-3 years of electricity consumption. For such calculations, a minimum of a year’s data is required and 3 years is optimal.
Questionnaires are used because they allow ease of comparability which is desirous in this analysis. Interviews and focus group data collection methods were considered and discarded due to the objectives of research. Also collection of electricity consumption data through the use of data loggers and other field instruments became unnecessary due to the availability of smart meters at the buildings. Electricity consumption data required is for a year at least for significant analysis. This issue made on-site electricity collection
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unfeasible for this research. Monthly bills from the central utility supplier showing payment and actual consumption records were used instead.
The self-administered questionnaire consists of nine sections and an introduction/consent form at the beginning. The nine sections are divided into:
1. Building characteristics 2. Occupancy schedule 3. Site sketch
4. Electricity from utility use
5. Electricity from alternative source use 6. Heating ventilation and air conditioning 7. Cooling load
8. Lighting load 9. Appliances
The fieldwork was conducted from November 2009 till March 2010 to record, calculate and estimate the input variables for the modelling and simulation. The classes of data are shown in Figure 3-9.
Figure 3-9 Data collection procedure