South Africa's source of energy in buildings is electricity from a struggling state utility running mainly on coal. But despite the constraints and the high cost, energy usage remains mostly unchecked and poorly managed in schools, whose attention is focused on reducing inequality in educational outcomes. School energy usages have been well studied in many countries but remain largely unexplored in South Africa. We use smart electricity meters to analyse the energy requirements of 16 schools across the country's polarised auence spectrum. Comparing their usage with that of schools from other countries, we nd that the auent South African schools have energy densities of 13% as much as those in North America, compared to the poor schools with a mere 5%. We observe a large di erence between the daily usage pro les of poor and auent schools: poor schools used only 40% of the energy used by auent schools. We ascribe this disparity to constrained infrastructure investment at the poor schools. We identi ed lighting as a major contributor to energy usage by schools, accounting for as much as 31% of the daily in-term usage by auent schools and 40% by poor schools, compared to 14 to 25% in developed countries. Since most schools still use uorescent lights, substantial savings can be achieved by replacing them with ecient LED lights. Keywords: School electricity; School budget; Energy usage; Smart meter; School performance; School energy pro les 1. Introduction Over the past decade, South Africans have su ered power blackouts and rapidly rising utility costs because of electricity system changes and crises of supply. Electricity tari s rose by 446% from 2008 to 2019 [1], a more than four-fold increase, outstripping in ation of 98% over the period. This has resulted in attempts by various actors to keep demand and costs down and improve energy eciency [2, 3]. It has also put nancial pressure on the education system and made schools anxious to reduce costs and minimise waste [4]. Failure to do so means that funding earmarked to improve the quality and equity of South Africa's education is channelled into paying electricity bills. Although South Africa spends more than 20% of government budget on education, spending on basic education is under pressure. The schools' budget per pupil decreased by 8% between 2010 and 2017, while electricity rates increased [5]. Schools use large numbers of state buildings and thus account for a large portion of the state's electricity bill. An estimated aggregated yearly energy usage of 2.2 GWh means that the cost impact of tari increases is substantial and diverts funding away from essential maintenance and infrastructure development. Apartheid has left South African schools a legacy of unequal distribution of facilities and resourcing. The quality of basic infrastructure varies widely and many schools fail to match up to national minimum standards [6, 7]. In 2020, 4,000 schools still had pit latrines. In 2018, of the 23,471 public schools, 20,071 had no laboratory (including a computer laboratory), 18,019 no library, 16,897 no internet and 9,956 no sports facilities. However, only 269 schools were without electricity, so it appears the electri cation of schools has been done quite successfully [6]. This electri cation rate is very high compared with the 80% of schools without electricity in sub-Saharan Africa. However, despite this high rate, pupils do not enjoy the bene ts of light, heating and modern teaching tools [8]. Corresponding author Email address: mjbooysen@sun.ac.za (M.J. Booysen ) Preprint submitted to ERSS July 6, 2020Preprint accepted for publication in Energy Research and Social Science 100m Property Buildings Quintile 5 Primary 600 learners Quintile 3 Primary 600 learners Quintile 1 Primary 1400 learners Quintile 5 Secondary 700 learners Quintile 4 Secondary 1200 learners Quintile 1 Secondary 1200 learners Figure 1: Satellite images of six of the schools in our study at the same zoom factor from Google Maps. The three top schools are primary, and the bottom three secondary. The schools are all public (i.e. government-owned) schools, and within a 2 km radius. Quintiles 5 to 1 represent decreasing quintiles of auence. The unequal distribution of facilities described above means that these bene ts remain unrealised in many South African schools. The Amended National Norms and Standards for School Funding, South African Schools Act No 84 of 1996, aimed to help reduce the historical inequities in the South African education system. For the purpose of government funding, schools are classi ed into ve quintiles according to their socioeconomic status, measured as average income, unemployment rate and general literacy in the school's geographical area [9]. Quintile 1 schools, still mostly serving black pupils, are the poorest, and Quintile 5 schools, many of them formerly white schools, are the most auent. Quintiles 1 to 3, which are non-fee-paying, receive the highest allocation per pupil and Quintile 5 schools the lowest, on the assumption that the parents can a ord fees. Studies have shown that despite this attempt to distribute the funding to the bene t of poorer schools, inequalities persist in terms of access to good education [5, 10, 11]. In this article we investigate electricity usage patterns in a sample of 16 South African schools in the Stellenbosch area, in South Africa's Western Cape province. Our sample includes all ve quintiles as de ned by the 1996 Act. The research forms part of a larger project that aims to support improved school management and policy making and help reduce the drain on the national electricity grid and damage to the environment. Within this context, the aim with this paper is to better understand how energy usage of the schools in our sample compare with schools in other countries, and to quantitatively explore to what extent there is a di erence between the auent and poor schools' usage. As an example, gure 1 captures some of the spatial di erences between six schools that formed part of this study, all within a 2 km radius. Inequalities in schools are not limited to infrastructure. Classrooms in poor schools are overcrowded [6]. Vandalism and theft, especially of computer equipment (in those schools lucky enough to have it), burden schools with replacement expenditure. Inadequate safety and security arrangements put pupils and sta at risk in crime-ridden areas. Some schools su er from bad nancial management and some have been found to be in municipal arrears [12, 13, 14]. These problems exacerbate poor learning conditions and outcomes in South Africa's basic education system. 2 Table 1: Literature review map Energy Occupancy and Technologies and assemblies management Operational requirements considered/mentioned in analysis Geography and sample Core focus and contribution kWh/m2GDP per capita Mechanical Lighting Envelope Reference Developing countries Slovenia (24 schools) Energy audit; role of insulation, energy saving lights and boiler219 26,124 No No Yes Yes Yes [15] Argentina (15 schools) Energy benchmarking for schools 121 11,684 Yes Yes Yes Yes Yes [16] Tehran (2 schools) Energy modelling and retro t implementation220 5,628 No Yes Yes Yes Yes [17] United Arab Emirates (1 school) Energy management modelling 438 43,005 Yes Yes Yes Yes Yes [18] Pretoria, South Africa (1 school) Infrastructural and operational data used to model electricity consumption in schools27 6,374 No Yes Yes Yes No [4] Jordan (1 school) Retro t strategies to improve building envelope and increase energy savings46* 4,242 No Yes Yes No Yes [19] China (17 schools) Found electricity to be the sec- ond highest energy source and cost122 9,771 Yes No No Yes Yes [20] Taiwan (231 schools) Three regression models for to- tal energy consumption, energy use intensity and energy use per pupil in school building - showed that air-conditioning and light- ing are heavy contributors to en- ergy usage33 50,500 No No Yes Yes Yes [21] Developed countries USA Advanced energy retro t guide N/M* 62,795 Yes Yes Yes Yes Yes [22] USA Energy usage in K-12 schools 360 62,795 Yes Yes Yes No No [23] Treviso, Italy (60 schools) Energy consumption of air heat- ing and hot waterN/M* 34,483 No Yes Yes No Yes [24] Canada (3 schools) Energy consumption analysis 251 51,358 Yes Yes Yes No Yes [25] Spain (2 schools) Investigated the reduction of emissions in schools in hot cli- mate conditions; suggested light- ing substitutions57 30,371 No Yes Yes Yes Yes [26] Spain (2 schools) Energy assessment method for low-carbon energy schools17 30,371 Yes Yes Yes Yes Yes [27] Belgium (30 schools) Developed benchmarks models of zero energy schools49 47,519 Yes Yes Yes Yes Yes [28] Sweden (7 schools) Benchmark energy model for (nearly zero grid energy) schools62* 54,608 No Yes Yes Yes Yes [29] 1.N/M: This information was not mentioned in the article. 2.*: These values do not represent the total school usage. however the school usage considered in the paper is mentioned. 3. Average values are used for energy intensity (kWh/m 2). 3 2. Energy eciency studies We rst review studies of energy eciency interventions and then those with a speci c focus on school energy usage. 2.1. Energy eciency interventions As buildings use about 40% of energy worldwide and are responsible for one third of greenhouse gasses, a large body of research has investigated ways to improve their energy eciency. The main ways of doing this are technological, organisational and behavioural [30, 31]. Technological interventions involve changing mechanical components, the lighting system and the building enve- lope (the outer shell that helps to regulate the indoor environment), and micro-generation (o -grid sources of energy generation such as integrated photovoltaic thermal systems). Heating, ventilation and air conditioning use the high- est proportion of energy in a building [32]. Interventions include converting or upgrading the existing system, or using natural ventilation for cooling and heating and moisture recovery, for example with enthalpy exchangers (en- ergy recovery ventilators) in the building's ventilation system. Lighting systems account for approximately 15% of building energy demand [32]. Interventions include installing state-of-the-art lighting components, such as LED, and installing sensors or automated lighting controls. Interventions involving the building envelope include using thermal mass elements (materials with the ability to store or absorb heat such as wall insulation and concrete), changing the building's window design, upgrading the fa cade in various ways, for example with a ventilated double skin, and using micro- generation systems [33]. Organisational interventions involve using real-time energy monitoring to identify unexpected levels of usage and benchmark typical energy use, so as to set a baseline for comparisons and standardisation [34, 35]. Behavioural interventions involve establishing an energy eciency culture in an organisation. Here various factors like occupancy behaviour, organisational culture and technology adoption play a role [36]. 2.2. Energy eciency and school lighting Table 1 summarises the studies reviewed for this paper, grouped according to country and mapped against the main energy eciency interventions discussed in section 2.1. Studies in both developing and developed countries have shown the bene t of compiling data sets and improving the visibility of school energy usage. Energy eciency interventions are complicated by schools' individual electricity arrangements, such as their power sources, equipment and usage pro les; by the seasons and the weather, which makes seasonal, monthly, weekly and daily analysis necessary [37]. Furthermore, literature has shown that the socio-economic status of schools also plays a role [10]. Lighting has consistently been found to account for a large percentage of schools' energy usage, for example 14% in the USA [22], and in Spain 21% [26] and 25% [27]. The percentage was 46% in a single Quintile 5 school in Gauteng, South Africa [4]. In a European life cycle assessment to investigate ways to reduce emissions in schools in hot climate conditions, Gamarra et al. [26] recommend energy eciency improvements in lighting and air-conditioning. In an analysis of school building energy usage in 17 schools in Tianjin, China, Ma et al. [20] found that schools' total energy consumption was large, despite they schools' relatively small energy use intensity compared to other public buildings. They found that the second highest energy source was electricity, which was also the second highest expense. Investigating the nal energy usage (monthly billed energy usage) of 231 schools in Taiwan, Wang [21] found that air-conditioning and lighting were heavy contributors. In Belgium, Attia et al. [28] developed benchmark models of near zero energy schools (these are schools with energy demand equal to or less than grid-supplied energy). Their analysis of four years' worth of monthly energy data found that two thirds was electricity usage. 3. Method Table 2 summarises the characteristics of the 16 schools in our sample. Although all within close range of each other, they di er substantially. The Quintile 5 schools tend to have more extensive buildings overall and a lower pupil to building size ratio, and all had hostels on the premises. We used smart meters to capture the schools' electricity usage. These were of two types: municipal smart meters and retro- t third-party smart meters. The measurements used were energy (kWh) and demand (kW and kVA), monitored at 10 to 30 minute intervals. These data were then downloaded from the cloud databases for analysis. To compare the schools' energy demand we measured energy usage intensity, usage per pupil, baseload usage and average usage. Since energy intensity is widely used to quantify and compare eciencies of buildings, it provides a 4 Table 2: Summary of the 16 schools in our data set. All except one are within a 4 km radius of the centre of the town of Stellenbosch. Primary/Secondary Quintile No of pupils Size of building [m2] Hostel (Y/N) Primary 1 1600 5160 N Primary 2 600 3874 N Primary 3 300 3133 N Primary 3 700 4344 N Primary 4 600 2263 N Primary 4 900 4850 N Primary 4 1000 2504 N Primary 5 600 5744 N Primary 5 850 5582 N Primary 5 900 8800 N Secondary 1 800 2348 N Secondary 2 150 628 N Secondary 4 1150 4917 N Secondary 5 550 7652 Y Secondary 5 700 8826 Y Secondary 5 1200 12703 Y useful yardstick with which to compare and contextualise South African schools' energy usage. We compared the schools' annual energy intensity with the gures reported in the literature, using annual energy usage per building size, given as kWh/m2/year. We used the ve quintile rankings to organise our sample into an auent group (Q5) and a poor group (Q1{4). We compared the two groups' daily usage pro les, expressed as energy used per time period (e.g. per hour) per pupil. We compared all the schools using this metric, but since the Quintile 5 secondary schools all had hostels, we also compared the primary schools separately. The pro les were created for the 24 hours of the day and normalised to the pupil number of the schools (Wh/pupil). We used the temporal pro le to identify signi cant usage characteristics at speci c times of day and reported them in kWh, kW and per pupil equivalents. Finally, we used average energy usage, reported in kWh, for in-term and out-of-term times, to compare distri- butions in our two groups of schools. For each period the weekday and weekend energy usage were recorded. This energy information over these periods was useful to identify usage and extra activity for the di erent schools. The term period was 1 October to 6 December 2019 and the out-of-term period was 7 December 2019 to 15 January 2020. 4. Results Figure 2 shows school energy intensities reported in the literature for di erent regions and countries in annual kWh =m2, compared with the results from our two South African groups. The results unequivocally demonstrate that the energy intensity of both our groups is substantially less than that of other regions. Two notable exceptions were one school each from two Mediterranean countries, Italy and Spain. The surface area was derived by summing the surface areas of all the buildings from Google Maps' satellite imagery, which does not take into account the second and third levels of buildings. However, since energy intensity decreases with increased surface area, the box plots representing the South African schools will only go lower when those levels are also considered. Moreover, the international studies did not say whether they included the multiple levels or not. The gure also highlights the stark di erence between auent and poor schools in South Africa. The median auent school's energy intensity is approximately an eighth of the energy intensity of schools in the USA and Canada; conversely, the median poor school's is a twentieth. Table 2 and Figure 1 show that this energy intensity di erence is not the result of poor schools having more extensive buildings. Figure 3 shows the hourly energy usage pro les for the two groups, with the shaded bands indicating the schools in each group with lowest and highest average aggregated usage per day. Figure 3(a) shows the distribution for all the schools in our set with hostels included, while Figure 3(b) excludes the secondary schools, and therefore includes no hostels. The auent schools use substantially more energy than the poorer schools. In fact, the auent school with the lowest usage uses substantially more than the poor school with the highest usage. For the full data set, the median auent school has an average in-term daily use of 643 Wh =pupil, while the median poor school uses only 28% as much, at 178 Wh =pupil. For the primary schools, the median poor school uses 40 % as much as the median auent school. The variation in the group of auent schools is wider than that of the poor schools, which can be explained by the fact that the three heaviest consuming schools have hostels. If we consider only the primary schools in Figure 5 /uni00000024/uni00000055/uni0000004a/uni00000048/uni00000051/uni00000057/uni0000004c/uni00000051/uni00000044/uni00000024/uni00000056/uni0000004c/uni00000044/uni00000028/uni00000058/uni00000055/uni00000052/uni00000053/uni00000048 /uni00000030/uni0000004c/uni00000047/uni00000047/uni0000004f/uni00000048/uni00000003/uni00000028/uni00000044/uni00000056/uni00000057 /uni00000036/uni00000024/uni00000003/uni00000034/uni00000018/uni00000003/uni00000010/uni00000003/uni0000004f/uni0000004c/uni00000057/uni00000011/uni00000036/uni0000004f/uni00000052/uni00000059/uni00000048/uni00000051/uni0000004c/uni00000044 /uni00000036/uni00000052/uni00000058/uni00000057/uni0000004b/uni00000003/uni00000024/uni00000049/uni00000055/uni0000004c/uni00000046/uni00000044/uni00000003/uni00000034/uni00000014/uni00000010/uni00000017/uni00000036/uni00000052/uni00000058/uni00000057/uni0000004b/uni00000003/uni00000024/uni00000049/uni00000055/uni0000004c/uni00000046/uni00000044/uni00000003/uni00000034/uni00000018 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 /uni00000015/uni00000014/uni00000015/uni00000014/uni0000001c/uni0000001a /uni00000014/uni00000014/uni00000017 /uni00000018/uni0000001c/uni00000018/uni00000015 /uni00000015/uni0000001a/uni00000015/uni00000017 /uni00000014/uni00000013Figure 2: Energy use intensities per year (kWh =m2=y) reported in the literature for schools in various countries and regions compared with the two school groups in our data set. Notes: 1. The vertical axis is logarithmic. 2. Grouping by region, where applied, is our own. 3. The South Africa sample from the literature (SA Q5 - lit.) is a single quintile 5 school in Gauteng, South Africa [4] . /uni00000013/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000015/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000017/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000019/uni0000001d/uni00000013/uni00000013 /uni00000013/uni0000001b/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000015/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000017/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000019/uni0000001d/uni00000013/uni00000013 /uni00000014/uni0000001b/uni0000001d/uni00000013/uni00000013 /uni00000015/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000015/uni00000015/uni0000001d/uni00000013/uni00000013 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 /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000050/uni00000048/uni00000047/uni0000004c/uni00000044/uni00000051/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000003 (a) All schools /uni00000013/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000015/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000017/uni0000001d/uni00000013/uni00000013 /uni00000013/uni00000019/uni0000001d/uni00000013/uni00000013 /uni00000013/uni0000001b/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000015/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000017/uni0000001d/uni00000013/uni00000013 /uni00000014/uni00000019/uni0000001d/uni00000013/uni00000013 /uni00000014/uni0000001b/uni0000001d/uni00000013/uni00000013 /uni00000015/uni00000013/uni0000001d/uni00000013/uni00000013 /uni00000015/uni00000015/uni0000001d/uni00000013/uni00000013 /uni00000003/uni00000013/uni00000015/uni00000013/uni00000017/uni00000013/uni00000019/uni00000013/uni0000001b/uni00000013/uni00000024/uni00000059/uni00000048/uni00000055/uni00000044/uni0000004a/uni00000048/uni00000003/uni0000005a/uni00000048/uni00000048/uni0000004e/uni00000003/uni00000048/uni00000051/uni00000048/uni00000055/uni0000004a/uni0000005c/uni00000003/uni0000004c/uni00000051/uni00000003/uni0000003a/uni0000004b/uni00000003/uni0000000b/uni00000049/uni00000052/uni00000055/uni00000003/uni00000048/uni00000059/uni00000048/uni00000055/uni0000005c/uni00000003/uni0000004b/uni00000052/uni00000058/uni00000055/uni0000000c/uni00000003/uni00000053/uni00000048/uni00000055/uni00000003/uni0000004f/uni00000048/uni00000044/uni00000055/uni00000051/uni00000048/uni00000055/uni00000034/uni00000018/uni00000003/uni00000050/uni00000048/uni00000047/uni0000004c/uni00000044/uni00000051/uni00000003/uni00000044/uni0000004a/uni0000004a/uni00000055/uni00000048/uni0000004a/uni00000044/uni00000057/uni00000048/uni0000001d/uni00000003/uni00000017/uni00000015/uni0000001a/uni00000003/uni0000003a/uni0000004b/uni00000012/uni0000004f/uni00000048/uni00000044/uni00000055/uni00000051/uni00000048/uni00000055/uni00000012/uni00000047/uni00000044/uni0000005c 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 /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000050/uni00000048/uni00000047/uni0000004c/uni00000044/uni00000051/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000003 (b) Only primary schools Figure 3: Time-of-usage pro les of average energy usage on in-term weekdays (term 4: 1 Oct to 6 Dec 2019) for auent schools (Q5) and poor schools (Q1{4). (a) captures all schools that include hostels, and (b) only primary schools. The shaded bands show the schools with the maximum and minimum usage for each type. 6 3(b), the variation bands are substantially narrower, but the inequality gap is still gaping, especially during school hours and extra-mural hours of 8am to 5pm. We found substantial di erences between the schools' energy usage in the early morning and night times. In the hours from midnight to 6am, the median auent school in Figure 3(a) has an average load of 10 kW (17 W =pupil). In comparison, the median poor school has an average load of 2 kW (7 W =pupil). The most obvious feature behind this di erence is the hostel, which contributes to the secondary schools' load. Although the di erence is diminished in Figure 3(b), as the primary schools have no hostels, it still persists: 11 kW (12 W =pupil) versus 4 kW (6 W =pupil). One explanation for this is the larger property sizes that require more lighting for night-time security. Another less obvious explanation is the presence of boreholes (for irrigation) and swimming pools, both of which require long hours of pump activity. The auent schools typically schedule these to run in the evenings to avoid peak time-of- usage tari s. In extreme cases, auent schools even have swimming pools that are heated with electricity. On the other hand, the poor schools have a low and at load pro le all the way through from midnight to the time when school starts. In the early evening, the pro les are also markedly di erent for the two groups. The auent schools have more variation until 9pm, probably because of evening sport, that uses ood lights, and cultural activities. The poor schools again have a mostly at pro le in the evenings, primarily due to security ood lights. All the schools show a slight increase from 7pm to 9pm, as security lights are turned on. The load pro les during school hours (Figure 3(a)) clearly show how the inequality gap between the haves and have-nots widens, with the midday peak of the median auent school at 28 kW (48 W =pupil) and that of the poor school at a mere 5 kW (11 W =pupil). For the primary schools only (Figure 3(b)), the gap is less pronounced: 24 kW (28 W =pupil) versus 7 kW (11 W =pupil). The reason for the gap between the auent and poor schools can partially be explained by the energy spent per pupil on lighting energy: there are more pupils per classroom in poor schools than in auent schools, so the energy spent on lighting per pupil will therefore be less. Moreover, our preliminary assessments have shown that lighting accounts for a median of 40% (approximately 500 lights per school) of a poor school's total energy usage, and also 31% (approximately 600 lights per school) of an auent school's total energy usage. This is in the same range as the 46% reported by the one existing South African study [4], but substantially di erent from the 21% reported by Gamarra et al. [26] and the 25% reported by Lizana et al. [27], both from Spain, a developed country. However, the stark di erences are caused by more than the lights. Auent schools have well-equipped sta rooms and tea rooms, larger school halls, air conditioners, audio-visual training aids, wireless communications and improved access to computers. The absolute temporal distributions are shown in Figure 4(a{d) for di erent periods, making it possible to compare the energy used by the schools (a) per weekday in term time, (b) per weekday during the holidays, (c) per weekend during the term, and (d) per weekend during the holidays. For all the periods reported, the median of the auent school is more than double that of the median of the poor schools. In fact, for the term-time weekdays, the poor school with the highest usage uses only 221 kWh while the auent school with the lowest usage uses 302 kWh. The median for poor schools for this period is 140 kWh, and the median for auent schools is 393 kWh { almost triple. This stark di erence is also observable for weekends in term time, with respective medians of 38 kWh and 112 kWh, which are explained by the wealthy schools' sport activities. Note the tennis courts, rugby elds, astro turf, swimming pools, and other facilities at Q5 schools in Figure 1. Energy used per day out of term times is also substantially di erent for the two groups. This means that even during the holidays, when no classes are being held at the school, the median auent school uses 74 % more than the median poor school (median Q1{4 = 68 kWh; median Q5 = 258 kWh). 5. Conclusion With South Africa's education system and energy generator both severely constrained, we set out to determine the extent to which South African schools use energy, which comes down to using electricity. We analysed the literature on energy usage at schools, comparing developed and developing countries separately (Table 1). From this literature search, we created a review map that compared the energy usage, energy man- agement, occupancy and technologies representative of the schools. Our data set of 16 schools covers a spectrum of auence in the town of Stellenbosch, South Africa. The di erences in usage that we found largely re ect the segregation line enforced during apartheid, and demonstrate that this distinction is still evident through an energy lens: auent schools (Quintile 5) are better resourced and thus better positioned to bene t from electri cation than poorer schools (Quintiles 1{4). We used municipal and third-party smart meters to collect high resolution energy usage data. Comparing average usages per year to those reported in the global literature, we found that Stellenbosch's auent schools 7 /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 /uni00000034/uni00000018/uni00000003/uni00000053/uni00000055/uni0000004c/uni00000050/uni00000044/uni00000055/uni0000005c/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 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(a) Average energy used per day on a weekday, in term. /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 /uni00000034/uni00000018/uni00000003/uni00000053/uni00000055/uni0000004c/uni00000050/uni00000044/uni00000055/uni0000005c/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 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 (b) Average energy used per weekday, out of term. /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 /uni00000034/uni00000018/uni00000003/uni00000053/uni00000055/uni0000004c/uni00000050/uni00000044/uni00000055/uni0000005c/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 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 (c) Average energy used during a weekend day, in term. /uni00000034/uni00000014/uni00000010/uni00000017/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 /uni00000034/uni00000018/uni00000003/uni00000053/uni00000055/uni0000004c/uni00000050/uni00000044/uni00000055/uni0000005c/uni00000003/uni00000056/uni00000046/uni0000004b/uni00000052/uni00000052/uni0000004f/uni00000056 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 (d) Average energy used per weekend day, out of term. Figure 4: Energy usage of the schools at di erent times. The Q5 primary schools are also shown in isolation, to remove the impact of hostels. In term: 1 Oct to 6 Dec 2019. Out of term: 7 Dec 2019 to 15 Jan 2020. 8 use substantially less energy per surface area than schools around the world. The median auent schools in our study reported energy intensities at an eighth of the schools in North America; the poor schools reported at a twentieth. The energy intensity of our South African schools is evidently substantially less than those in other countries for both auent and poor schools. This result holds for schools in warmer and colder climates. We therefore contend that this metric, which is typically used to measure building eciency, in this case belie a relative lack of investment in infrastructure, rather than incredibly high eciencies. Such lacking infrastructure include climate control (heating and/or cooling) to improve learning, but more importantly probably expose a shortfall in information technology equipment, mainly computers, especially in the poorer schools. Moreover, poor schools in our sample use substantially less than auent schools. As with the lower energy intensities than those reported in international studies, this apparent lower usage by the poor schools is not due to energy eciency, but the lack of equal access to energy-consuming resources, such as laboratories, computers and sporting facilities. We compared the daily energy usage pro les of the two groups of schools in our data set for one term, expressed as energy per pupil per year. Time-of-usage pro les were generated that featured all the schools and only the primary schools, grouped according to their levels of auence. The pro les revealed the inequality gap between schools, with the poorer quintiles using 28% less than the most auent quintile in the all-schools comparison and 40% in the primary schools comparison. Moreover, the temporal pro les allowed for time-based investigation of two speci c times. The average baseload of auent schools was ve times larger than that of the poorer schools. The reason for the auence-energy gap during the day can be explained by sustained low investment in infrastructure at the poor schools. Infrastructure investment takes time, which was also the case for the auent schools. The stark di erences observed here has also taken a long time to accrue, and will accordingly take time to correct. A large contributor to high usage during the day was identi ed as outdated uorescent lights. Lighting was responsible for 31% of daily use at the auent schools in our sample, and a median of 40% at the poorer schools; substantially more than the 14% to 25% observed in other countries. The energy usage for the two groups was also compared on a day-basis for the weekdays and weekends during the term and during the holiday. For all reported periods, comparison of median usage showed that the auent schools' usage was more than double that of the poorer schools. This is further indication of the inequities in the two groups of schools to bene t from energy further putting South Africa's poor children at a disadvantage. For future work, these energy-use inequalities could be further investigated; also how various energy eciency interventions may result in savings for especially poor schools where lighting make up the highest percentage of electricity bills. We thus suggest a study where the lighting systems of South African schools be rolled over to energy ecient lights. We also acknowledge that monitoring and awareness are crucial in reducing energy usage of humans and thus suggest using organisational and behavioural interventions in combination with the above. An earlier pilot study was conducted using both these interventions and yielded 11 to 14% energy savings for auent schools who took part. Acknowledgement The authors would like to thank the MTN South Africa for their nancial and technical support through the MTN Mobile Intelligence Lab, Stellenbosch University Social Impact for their nancial support; Eskom for their nancial support through the Tertiary Education Support Programme; Stellenbosch municipality for the provision of information; and the 16 schools for their participation.