- Original article
- Open Access
Electricity peak demand in Uganda: insights and foresight
© The Author(s). 2016
- Received: 6 February 2016
- Accepted: 19 September 2016
- Published: 10 October 2016
Availability of reliable energy supply plays a critical role in the social, economic, and cultural transformation of society. The Uganda electricity sector has suffered long standing supply side constraints that resulted in suppressed demand and outages. Recent developments, including the completion of the 250 MW Bujagali project in 2012, have resulted in sustained growth in peak demand. However, this growth in peak demand appeared to stagnate by 2013. This study examines the recent trends as well as forecast the medium term path of electricity peak demand in Uganda.
This study uses descriptive data exploration analysis and polynomial functions augmented by empirical estimations of structural break equations to account for the observed trends in electricity peak demand. The study applies the double exponential forecasting model to forecast total peak electricity demand.
The results show that the recent surge in electricity peak demand is due to increased electricity exports. Moreover, the results show a shift in electricity demand from peak to nonpeak time-of-use, possibly due to changing consumption patterns in the industrial sectors.
The study draws two major conclusions. First, the growth in Uganda’s electricity demand in general and peak demand in particular has not stagnated as such but rather partially shifted from peak to nonpeak time-of-use zone. Second, electricity exports have contributed to growth of electricity peak demand. Importantly, higher electricity exports need to be considered in line with the system capacity given the current electricity spinning reserves of Uganda are less than 15 % of Uganda current installed capacity.
- Electricity Consumption
- Electricity Demand
- Electricity Sector
- Peak Demand
- Demand Side Management
Availability of reliable energy supply is critically important for economic growth, poverty reduction, and the social and cultural transformation of society [1–3]. Deficient electricity infrastructure curtails social and economic development . Proper energy planning ensures the sustainable development of energy systems that meet the growing energy demands. Electricity peak load modeling and forecasting is an important aspect of energy sector planning and management . Electricity peak demand refers to the highest amount of electricity that an electrical system must supply to all its customers at any given time, in any one period such as a month [6, 7]. Peak demand occurs when the demand for electricity sharply increases (spikes) in magnitude compared to a normal trend. The spike in demand can be short-lived or last for a longer period.
Unmet electricity peak demand that outstrips infrastructure generation, transmission, and distribution capacities stresses networks and may result in short-term drop in voltage leading to outages . Unmitigated electricity outages have significant undesirable social and private impacts [9–14].
However, growth in peak demand appeared to stagnate by 2013, averaging 493 MW. This apparent stagnation in peak demand concerned stakeholders, including government, especially given the ongoing efforts to increase electricity generation that have primarily focused on exploiting hydropower by expediting the construction of the 183 MW Isimba and 600 MW Karuma Hydro Power Projects whose expected completion dates are 2017 and 2019, respectively.
Despite these concerns, studies on electricity peak demand have attracted little attention. Okoboi and Mawejje  have attempted to account for this stagnated peak electricity demand by examining the impact of adoption of power factor correction technology. Other studies have evaluated the possible role of renewable energy—such as solar—as a substitute for grid supplied electricity [17, 18]. Despite these efforts, however, more analysis is required to fully understand the dynamics of electricity peak demand in Uganda. This is even more important considering that the country has prioritized electricity generation as critical for greater economic performance and social transformation .
Against this background, this paper examines the recent trends in electricity peak demand in Uganda as well as forecast the medium term path of peak demand. We decompose the trend in electricity peak demand, in particular at transmission level, to try to establish the underlying drivers of the trend. Specifically, this paper considers the following objectives: (1) examine the trend of peak demand; (2) assess the significance of electricity exports in total peak demand; (3) examine the likely shift in domestic electricity consumption from peak to other (shoulder and off-peak) time-of-use (TOU) zones; (4) examine the relationship between industrial production and domestic electricity peak demand; and (5) forecast the medium term path of electricity peak demand in Uganda.
Using descriptive data exploration analysis and polynomial functions augmented by empirical estimations of structural break equations, we show that the recent surge in electricity peak demand is due to increased electricity exports. Moreover, we show a shift in electricity demand from peak to nonpeak time-of-use, possibly due to changing consumption patterns particularly in the industrial sector.
The remainder of this paper is organized as follows: The “Context” section provides the context with regard to the Uganda electricity sector. The “Methods” section introduces the methods used in the analysis. Results are presented and discussed in the “Results and discussion” section. The “Conclusions” section provides the conclusions and recommendations.
Timeline of Uganda’s electricity sector reforms
Government approves the power sector restructuring and privatization strategy
The new electricity Act is passed
The Electricity Regulatory Authority becomes operational
The Uganda Electricity Board is unbundled and three companies created and registered namely: UEGCL, UETCL, and UEDCL
Concessions for generation and distribution are advertised
Concession for generation awarded to Eskom Enterprises
Appointment of the Rural Electrification Board to oversee the Rural Electrification Trust Fund (RETF)
UMEME awarded concessionaire to operate for 20 years to purchase electricity in bulk from UETCL and distribute it along low voltage electricity lines to individual customers
Installed electricity capacity in MW 2010–2015
Kasese Cobalt Company Ltd
Kakira sugar works
Kinyara sugar works
To date, the generation capacity eclipses the pre-reform installed capacity—a milestone that only a few African countries have managed .
In a bid to save the scarce available resources and instead focus attention on securing future electricity supply, the Government scrapped subsidies to the electricity consumers in 2012. Subsequently, generation projects such as Karuma (600 MW), Ayago (600 MW), and Isimba (183 MW) have been earmarked for immediate construction.
Developments in distribution
Distribution companies in Uganda
Umeme inherited customers that were once served by UEB and was leased the UEDCL assets under a 20-year concessional arrangement and controls 97 % of the distribution market in Uganda.
Ferdsult operates and maintains a rural electricity distribution network concessionaire under a 10-year agreement with the Rural Eelectrification Agency. Areas of operation include the districts of: Kibaale, Kyenjojo, Rukungiri, Kanungu, Ntugamo, Isingiro, Rakai, and Masaka. Ferduslt pioneered the pre-paid metering system in Uganda and currently serves about 10,000 consumers.
West Nile Electricity Company (WENRECO)
WENRECO operates an off-grid distribution network in the Northwestern districts of Arua, Paidha, Nebbi, Koboko, Maracha, Zombo, and Yumbe. The company operated the 3.5 MW Nyagaka HPP and served about 4000 customers by March 2013.
Coop. Society (BECS)
BECS runs the distribution concessionaire in Bundibugyo district since 2009. Accordingly, BECS took charge of electricity distribution, grid maintenance, and managing the revenue from power consumers. Currently, BECS serves about 1500 customers.
Pader - Abim Energy Cooperative
Serves about 1500 customers in Pader, Abim, and Agago districts.
Kilembe Investments Limited (KIL)
KIL runs a 10-year concessionaire to distribute and sell electricity in the Districts of Kasese, Rubirizi, and surrounding areas. The license runs for 10 years and is renewable. Currently KIL serves about 2000 customers on the pre-payment system. KIL intends to introduce solar energy for users in isolated areas.
Kygegwa Rural Electricity Cooperative Society
Electricity access and consumption
In addition to social and cultural barriers, the major constraints to rural electrification are problems with rural isolation, power theft, insufficient supply, and the high costs that have inhibited rural communities from gaining access to electricity. Consequently, Uganda still compares unfavorably with its region neighbors with regard to household access to electricity. Indeed, Uganda’s electrification rate of 14 % is lower than that of Kenya which stands at 29 % and Tanzania which is 16 % (Fig. 6).
Reliability of electricity supply
Extent of electricity challenges in Uganda
Number of electrical outages in a typical month
Duration of a typical electrical outage (hours)
Losses due to electrical outages (% of annual sales)
Average losses due to electrical outages (% of annual sales)
Percentage of firms owning or sharing a generator
Proportion of electricity from generator, %
Days to obtain an electrical connection (upon application)
Percentage of firms identifying electricity as a major obstacle
In line with industry best practices, the Electricity Regulatory Authority (ERA) has promoted new initiatives such as prepaid metering and aerial bundle conductors (ABC) to enhance energy use efficiency and reduce peak demand at low voltage level. In addition, the Government has partnered with the distribution companies to distribute free energy saving bulbs to electricity consumers as a demand side management (DSM) option to shave-off peak demand.
With regard to sector regulation, ERA has continued to pursue incentive-based regulation as an effective DSM option to mitigate the forecasted growth in peak demand until the planned small renewable energy resources under the GETFIT Project are expected to be commissioned between 2017 and 2018 start supply power to the grid.
The data used in this study is from three sources, namely: Uganda Electricity Transmission Company Limited (UETCL), Umeme Limited, and Uganda Bureau of Statistics (UBOS). System data on power and energy purchases from electricity generation companies, sales to distribution companies, and imports and exports from and to Kenya, Tanzania, Rwanda, and Democratic Republic of Congo was obtained from UETCL. Distribution data on energy sales to different customer categories (Domestic, Commercial, Medium and Large Industries, and Street Lighting) at different time-of-use (TOU) periods was obtained from Umeme Limited. The proxy for industrial production—the Index of Industrial Production (IOP)—was obtained from UBOS. The data from UETCL and Umeme are monthly and span the period January 2011 to August 2014. The data from UBOS are quarterly and span the period 2011Q1 to 2014Q1.
Descriptive statistics of the variables
Unit of measure
Total peak demand
Domestic peak demand
Total energy purchases
System peak demand
System nonpeak demand
Other distributor purchases
Total purchases by distributors
UETCL total sales
System energy loss
Umeme offpeak sales
Umeme shoulder sales
Umeme peak sales
Umeme domestic sales
Umeme total sales
Umeme energy losses
Index of industrial production
Average domestic system peak
Maximum domestic system peak
The descriptive statistics indicate that between January 2011 and August 2014, the registered system electricity peak demand has ranged between 428 and 550 MW with average peak demand of 482 MW. The energy losses average 3.6 % of total purchases at transmission system level while at distribution level (Umeme Limited), energy losses average 26 % of purchases from the system operator.
To improve the efficiency of the results from the analysis, the data were transformed into natural logarithms and the econometric analysis involved robust and bootstrapped standard errors where applicable.
Trend and descriptive statistical analyses were used to examine the trends of peak demand;
Bootstrapped linear regression models were used to assess the significance of electricity exports in total peak demand. Bootstrapped methods were adopted because they basically replicate the observations to the desired level and improve the efficiency of the estimates especially where the sample is relatively small .
tp = total peak demand, measured in megawatt (MW);
ex = total electricity exports demand, measured in gigawatt hours (GWh);
umeme = UETCL energy sales (GWh) to Umeme that distributes up to 96 % of electricity generated in Uganda;
other = UETCL energy (GWh) sales to other electricity distribution companies in Uganda that include Uganda Electricity Distribution Company Limited (UEDCL), Ferdsult Engineering Services Limited (FESL), Kilembe Investment Limited (KIL), Bundibugyo Electricity Cooperative Society (BECS), Pader-Abim Community Multi-purpose Coperative Society (PACMECS), and Kyegegwa Rural Electricity Cooperative Society (KRECS);
loss = energy losses (as a percentage total energy sales by UETCL—the system operator) experienced at transmission level;
upk = UETCL energy sales to Umeme at peak (18:00–23:00 h) TOU zone;
opk = UETCL energy sales to Umeme at off-peak (23:00–05:00 h) TOU zone;
ush = UETCL energy sales to Umeme at shoulder (05:00–18:00 h) TOU zone;
ε = error term representing any other factors not included in the equation but may have an impact of peak demand; and
β = parameters to be estimated while i = 1, 2,…, n is the number of observations from first to the last (n).
Fractional polynomial functions were estimated to determine the likely shift in domestic electricity consumption from peak to other (shoulder and off-peak) time-of-use (TOU) zones. The advantage with fractional polynomial functions is that they use the full information and search for the optimal functional form within a flexible class of functions .
pk = system peak demand, measured in gigawatt hours (GWh);
np = system nonpeak (shoulder and off-peak) demand (GWh);
d = the dummy variable; d = 1 if time period is January 2011 to December 2012, and d = 0 if time period is January 2013 to December 2014.
np*d = interaction term between the explanatory variable (np) and the dummy variable (d);
ε, z, and w = errors terms for the respective equation specifications above; and
α, γ and δ = parameters to be estimated while i = 1, 2,…, n is number of observation from first to the last (n).
GLM and fractional polynomials were used to estimate the relationship between industrial production and domestic electricity peak demand. With respect to the GLM, the estimated model is stated in Eq. 4.
dpk = domestic average or maximum peak demand, measured in GWh;
iop = index of industrial production, which measures (as percentage) performance of the industrial sector in current quarter compared to previous quarter given the base period.
μ = parameters to be estimated while i = 1, 2,…, n is number of observations from first to the last (n).
ε = error term representing any other factors not included in the equation but may have an impact of peak demand; and
The double exponential smoothing methods were applied to the predicted values of total peak demand derived from the estimated Eq. (1) to predict the medium term path of electricity peak demand.
y t = actual total peak in time t;
α = constant-process smoothing constant;
β = trend-smoothing constant;
C t = smoothed constant-process value for period t;
T t = smoothed trend of total peak demand for period t;
F t = forecast total peak demand for period t + 1;
t = current time period; and
t − 1 = previous time period.
Trends in electricity peak demand
Following the relatively stable electricity peak demand in 2013, many stakeholders in Uganda’s ESI expected the trend of peak demand to remain stable or even decline in 2014. These expectations were based on a 2014 review of end-user TOU tariff weighting factors that were increased from 10 to 15 % during peak TOU zone and downwards from −10 to −15 % during off-peak TOU zone compared to shoulder TOU zone.
Results from the 9-month trend of the peak demand in 2014 show that peak demand has consistently increased in 2014 compared to 2013 as indicated in Fig. 1. In May 2014, peak demand reached 550 MW thereby surpassing the highest record of 544 MW attained in December 2012. A comparison of the total peak demand to domestic demand suggests that the recent upsurge of total peak demand may be associated with increased exports of energy by UETCL given that 2014 domestic peak demand remained fairly and comparable to that of 2013.
Effect of electricity exports on total peak demand
As stated in the background, one of our objectives of this study was to examine the magnitude and impact of energy exports on the peak demand. The results of this analysis are contained in Table 6.
Table 6 presents the bootstrapped regression of total peak demand against UETCL disaggregated energy sales to domestic and export markets. In the first regression, domestic sales are disaggregated into sales to Umeme Limited, that is, the major distributor with about 96 % share of the domestic markets; other distributors; and UETCL technical losses. In the second regression, energy sales to Umeme are further disaggregated by TOU—that is, peak, shoulder, and off-peak sales.
Bootstrapped regression results
Dependent variable = total peak demand
Sales to Umeme (log)
Peak Sales Umeme (log)
Off-Peak Sales Umeme (log)
Shoulder Sales Umeme (log)
Sales to other dist. (log)
TX energy loss (log)
Increase in energy exports, sales to Umeme and sales to other distributors by UETCL, has a statistically significant (p < 0.01) impact on peak demand. In addition, an increase in total net exports by 10 GWh has 0.8 % impact on peak demand whereas 10 GWh increase in energy sales to Umeme leads up to 3.5 % increase in peak demand and 10 GWh increase in energy sales to other electricity distributors leads up to 0.9 % increase in peak demand.
In the second regression in Table 6, the results indicate that when UETCL energy sales to Umeme are disaggregated into peak, shoulder, and off-peak sales, there is a positive relationship with peak demand but the relationships are not statistically significant. In the same regression, the coefficients of UETCL energy exports and sales to other distributors have the same magnitude, impact, and statistical significance on peak demand as that in regression 1.
Relationships between electricity peak to nonpeak demand
In an effort to understand the likely causes of the relatively stable peak demand in 2013—which was expected to continue in 2014, some stakeholders in the ESI pointed to the possible shift in energy consumption from peak TOU zone to other (shoulder and off-peak) TOU zones. When there is a shift in energy consumption from peak to nonpeak TOU zones, this implies that on the one hand there would be a decrease in the growth of energy sales at peak TOU and on the other hand an increase in growth of energy sales at nonpeak TOU zone. This change in pattern of peak vis-à-vis nonpeak demand can be observed either at transmission and/or distribution level.
In order to make the robust comparisons, in Fig. 8, actual peak demand data has been multiplied by a scale of 2. The depiction of the graphs in Fig. 8 suggests that in 2011, peak demand declined faster and likewise increased faster in 2012 and for nonpeak demand on the other hand in 2011, declined mildly and as well increased mildly in 2012, leading to some sort of catch-up by peak demand. In the case of peak demand growth in 2013 and 2014, the graph indicates that it was generally linear, positive, and low. On the other hand, growth in nonpeak demand is also linear, positive, and low in 2013 but somewhat doubled in 2014.
Structural break regression of relationship between system peak and nonpeak demand
Dependent variable = peak energy sales
Nonpeak energy sales (NP)
M1_Before Jan 2013 (Jan 2011–Dec 2012)
Robust std. err.
Nonpeak energy sales (NP)
M2_After Jan 2013 (Jan 2013–Sept 2014)
Nonpeak energy sales (NP)
Results in Table 8 indicate a strong relationship (adjusted R square = 87 %) between peak and nonpeak demand. The dummy and interaction terms are statistically significant (p < 0.01), thereby suggesting that there is a statistically significant difference in the magnitude of the coefficients of the individual regression results that are shown in the lower panel of Table 8.
The coefficient for the first regression is 1.3 while that for the second regression is 0.51. This implies that in the period January 2011 to December 2012, a unit (1 GWh) increase in nonpeak demand was matched by 1.3 GWh increase in peak demand. On the other hand, in the period January 2013 to August 2014, a unit (1 GWh) increase in nonpeak demand was matched only 0.51 GWh increase in peak demand. This therefore suggests that there might be a decline in peak demand in the later period—which demand has shifted to nonpeak TOU zones, given the fact total energy demand has consistently increased in the reference period.
F test results that D = 0 and (NP*D) = 0
Prob > F
The slight shift in electricity consumption from peak to nonpeak TOU zone may be due to the incentive-based regulatory regime offered by the Electricity Regulatory Authority (ERA) to industrial consumers. The incentive regime involves lower tariffs at off-peak TOU and high tariffs at peak TOU. The other incentives and disincentives to industrial consumers of electricity involve Reactive Energy Reward to industrial consumers with efficient energy using equipment and Reactive Energy Charge to industrialists with inefficient power using equipment. The incentives and disincentive above are besides the maximum demand charges2 that ERA has set for industrialists.
Relationship between industrial production and domestic peak demand
GLM estimates of the relationship between IOP and domestic peak demand
Av. peak (log)
Max. peak (log)
Bootstrap std. err.
Number of obs.
Wald chi2 (1)
Prob > chi2
The GLM estimates in Table 10 indicate that the IOP correlates fairly well with average domestic peak demand than with maximum domestic peak demand. In terms of impact, the results indicate that a 10 % increase in the index of industrial production is associated with 6.6 % increase in average domestic peak demand and 5.8 % maximum domestic peak demand spikes in the country. The results are statistically significant at less than 5 % level. Based on this statistical relationship, we can conclude that industrial production is an important driver of peak electricity demand.
Medium term forecast of total peak demand in Uganda
The forecast considered three scenarios of Uganda’s medium term peak demand as follows: normal growth, accelerated growth that is 5 % above the forecasted normal growth, and suboptimal growth that is 2 % below the forecasted normal growth (Fig. 9). The forecasted normal growth in total electricity peak demand is based on the historical data while the forecasted accelerated growth scenario is based on Uganda Investment Authority (UIA) projections3 of growth in industries by both local and/or foreign investors in agriculture, construction, and mining especially as the country gears into the development stage of the oil and gas industry.
In the medium term, suboptimal growth in electricity peak demand may materialize if there are significant regional geopolitical risks that may curtail export demand for industrial products. In addition, the completion of energy projects in Uganda’s neighboring countries, such as Kenya’s Olkaria-Lessos-Kisumu Transmission Lines, may lead a decline in the recent upsurge in Uganda’s electricity exports to Kenya (Figure 12 in Appendix). This may to lead to a reduction in electricity peak demand given the previously observed close relationship between electricity exports and peak demand.
Results in Fig. 10 indicate that under the normal growth scenario, electricity peak demand is forecast to rise to 900 MW by January 2021. Under the accelerated growth scenario, our model predicts peak demand to reach an average of 950 MW by January 2021. Finally, under the suboptimal growth scenario, the model predicts January 2021 peak demand to be 850 MW.
This paper set out to provide insights and forecast in Uganda’s electricity peak demand. Specifically, the paper set out to examine the impact of Uganda’s energy exports on electricity system peak demand, the impact of industrial production on domestic electricity peak demand, and the likelihood of a shift in electricity peak demand from peak to nonpeak TOU. Furthermore, the paper set to forecast the medium term path of electricity system peak demand up to January 2021.
The study used a combination of descriptive and empirical estimations of structural models. Results indicate that the recent upsurge in Uganda’s electricity exports, particularly to Kenya, has had a significant bearing on system peak demand. The results also confirm that there is a positive and significant relationship between the industrial production index and domestic peak demand—given that industrial electricity demand is a derived demand—based on economic activity. In the case of a likely shift in peak demand, the results indicate a slight shift in electricity consumption in 2013–2014 from peak to nonpeak time-of-use zone. Finally, results from the forecast model predict that by January 2021, system peak demand will be in a range of 950 MW (accelerated growth scenario) and 850 MW (suboptimal growth scenario).
These results provide a number of conclusions and implications for policy. First, the growth in Uganda’s electricity demand in general and peak demand in particular has not stagnated as such but rather partially shifted from peak to nonpeak time-of-use zone. Second, whereas electricity exports bring additional revenues to the electricity transmission system operator, higher exports need to be considered in line with the system capacity given the current electricity spinning reserves of Uganda are less than 15 % of Uganda current installed capacity of about 870 MW. Increased energy security and sufficiency in the export market is likely to reduce power imports from Uganda. Third, the apparent shift in electricity consumption from peak to nonpeak TOU zone, particularly in the industrial sector, is a step in the right direct direction in so far as it reduces the peak load that is likely to be unsustainable in the near future given the country’s spinning reserves.
For additional information on ERA incentive-based tariffs, visit http://www.era.or.ug/index.php/component/content/article/94-general/176-umeme-ltd-tariffs.
For additional information investment projects coming into Uganda, see for example UIA 2013/14 Investment Abstract at: http://www.ugandainvest.go.ug/wp-content/uploads/2016/02/investment_abstract_2013_14_revised_Nove_2014.pdf .
This study benefited from insightful discussions and comments from colleagues at the Electricity Regulatory Authority and the Economic Policy Research Centre. We are grateful to two anonymous referees for useful comments.
GO conceptualized the study and contributed towards the data analysis. JM contributed towards the drafting of the manuscript and refining the paper. All authors read and approved the final manuscript.
GO is the Director of Economic Regulation at the Electricity Regulatory Authority. JM is a Research Analyst at the Economic Policy Research Centre with research interests in development, macroeconomics, and natural resources.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Oyedepo SO (2012) Energy and sustainable development in Nigeria: the way forward, Energy. Sustainability and Society 2(1):1–17View ArticleGoogle Scholar
- Payne JE (2010) A survey of the electricity consumption-growth literature. Applied energy 87(3):723–731View ArticleGoogle Scholar
- Rögnvaldur H (2009) Energy and GDP growth. International Journal of Energy Sector Management 3(2):157–170View ArticleGoogle Scholar
- Calderon C (2008) Infrastructure and growth in Africa. Africa Infrastructure Country Diagnostic, Working Paper 3, World Bank, Washington, DCGoogle Scholar
- Alfares HK, Nazeeruddin M (2002) Electric load forecasting: literature survey and classification of methods. International Journal of Systems Science 33(1):23–34MATHView ArticleGoogle Scholar
- Stern F (2013) Peak demand and time-differentiated energy savings cross-cutting protocols, subcontract report no. NREL/SR-7A30-53827, in “The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures”. National Renewable Laboratory. http://energy.gov/sites/prod/files/2013/11/f5/53827-10.pdf. Accessed 15 Nov 2014.
- Gross G, Galiana FD (1987) Short-term load forecasting. Proceedings of the IEEE 75(12):1558–1573View ArticleGoogle Scholar
- Chao H (1983) Peak load pricing and capacity planning with demand and supply uncertainty. The Bell Journal of Economics 14(1):179–190View ArticleGoogle Scholar
- Anderson R, Taylor L (1986) The social cost of unsupplied electricity: a critical review. Energy Economics 8(3):139–146View ArticleGoogle Scholar
- Lee KS, Anas A, Oh GT (1999) Costs of infrastructure deficiencies for manufacturing in Nigerian, Indonesian and Thai cities. Urban Studies 36(12):2135–2149View ArticleGoogle Scholar
- Reinikka R, Svensson J (2002) Coping with poor public capital. Journal of development economics 69(1):51–69View ArticleGoogle Scholar
- Kateregga E (2009) The welfare costs of electricity outages: a contingent valuation analysis of households in the suburbs of Kampala, Jinja and Entebbe. Journal of Development and Agricultural Economics 1(1):1–11Google Scholar
- Allcott H, Collard-Wexler A, O’Connell SD (2014) How do electricity shortages affect productivity? Evidence from India, National Bureau of Economic Research, Working Papers, No. 19977Google Scholar
- Fisher-Vanden K, Mansur ET, Wang QJ (2015) Electricity shortages and firm productivity: evidence from China’s industrial firms. Journal of Development Economics 114:172–188View ArticleGoogle Scholar
- Mawejje J, Munyambonera E, Bategeka L (2013) Powering ahead: the reform of the electricity sector in Uganda. Energy and Environment Research 3(2):126–138View ArticleGoogle Scholar
- Okoboi G, Mawejje J (2016) The impact of adoption of power factor correction technology on electricity peak demand in Uganda. Journal of Economic Structures 5(1):1–14View ArticleGoogle Scholar
- Twaha S, Ramli MA, Murphy PM, Mukhtiar MU, Nsamba HK (2016) Renewable based distributed generation in Uganda: resource potential and status of exploitation. Renewable and Sustainable Energy Reviews 57:786–798View ArticleGoogle Scholar
- Twaha S, Idris MH, Anwari M, Khairuddin A (2012) Applying grid-connected photovoltaic system as alternative source of electricity to supplement hydro power instead of using diesel in Uganda. Energy 37(1):185–194View ArticleGoogle Scholar
- Government of Uganda (2015) Second National Development Plan 2015/16–2019/20. National Planning Authority, Republic of UgandaGoogle Scholar
- Engurait SP (2005) Power sector reforms in Uganda: status and achievements. International Journal of Global Energy Issues 23(2):110–118View ArticleGoogle Scholar
- Karekezi S, Kimani J, Mutiga A, Amenya S (2004). Energy services for the poor in Eastern Africa Sub Region: energy access study of East Africa. Global Network on Energy for Sustainable Development. African Energy Research Network.Google Scholar
- Mawejje J, Munyambonera E, Bategeka L (2012) Uganda’s electricity sector reforms and institutional restructuring. EPRC Research Series No. 89, Kampala: Economic Policy Research CentreGoogle Scholar
- Mawejje J (2014) Improving electricity access in Uganda. Kampala: Economic Policy Research Centre. Policy Brief No. 43Google Scholar
- Mawejje J, Nampewo D (2012) Perceptions of Ugandan business executives on the current and expected business economic conditions, EPRC Business Climate Index Issue 1. Economic Policy Research Centre, KampalaGoogle Scholar
- Efron B (1979) Bootstrap methods: another look at the jacknife. The Annals of Statistics 7(1):1–26MathSciNetMATHView ArticleGoogle Scholar
- Royston P, Sauerbrei W (2008) Multivariable model-building—a pragmatic approach to regression analysis based on fractional polynomials for continuous variables volume 777. John Wiley & Sons Ltd, ChichesterMATHGoogle Scholar
- Clogg CC, Petkova E, Haritou A (1995) Statistical methods for comparing regression coefficients between models. American Journal of Sociology 100(5):1261–1293View ArticleGoogle Scholar
- Mawejje J, Mawejje ND (2016) Electricity consumption and sectoral output in Uganda: an empirical investigation. Journal of Economic Structures 5(21):1–16Google Scholar