How basic access to electricity stimulates temporally increasing load demands by households in rural developing communities

Opiyo, Nicholas

doi:10.1016/j.esd.2020.09.006

Cited by 12 publications

(13 citation statements)

References 8 publications

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“…Using the mean household income, de Rezende Francisco et al [27] produced a formula that tries to predict the increase of income due to the increase in electricity consumption for Brazil, achieving an R 2 value between 0.91 and 0.85. The work from Opiyo, N. [28] presents results that support the claim that new electrification temporally stimulates an increase in power demand in Sub-Saharan Africa. The present study makes use of the formula from Rezende Francisco et al [27], modified for Mali under the assumption that both populations behave similarly on the electricity consumption levels considered (more on this step appears in Section 2).…”

Section: Introductionmentioning

confidence: 60%

Rural Electrification Pathways: An Implementation of LEAP and GIS Tools in Mali

Sessa

Bhandari

2021

Energies

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Remote rural populations do not often have the luxury of viable multisource electricity generation systems. Considering fossil fuels for remote populated areas is not often a viable option due to the fuel transportation costs and the population’s socioeconomic status. Extending the grid is often economically prohibitive. This paper proposes possible ways in which Mali could increase the rate of population with access to electricity by 2050 using Low Emission Analysis Platform (LEAP) and geographical information tools. The current energy situation is assessed, and multiple demand and supply scenarios are created to find the most viable option in environmental and economic dimensions. A minimum of 50% reduction of biomass consumption in the residential sector and a maximum of 71% was achieved through the combination of grid extension and decentralized solar PV. Solar PV becomes the preferable option when enough time for the effects of electricity on income is given. When these effects are not present, solar PV is still a better option, as the amount of biomass replaced with electricity is reduced.

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Section: Introductionmentioning

confidence: 60%

Rural Electrification Pathways: An Implementation of LEAP and GIS Tools in Mali

Sessa

Bhandari

2021

Energies

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“…Lower income households were less likely to have upgraded than their higher income counterparts (FET, p-value: 0.003). Prior studies found mixed evidence of upgrades, with Opiyo [48] observing a high level of upgrades compared to findings by Bisaga and Parikh [49]. The majority of participants (76%) wanted to upgrade to other appliances in the future.…”

Section: Recommend Shsmentioning

confidence: 83%

“…Similar to sign-up reasons, more lights and the ability to watch television are among the top upgrade reasons, with televisions being the most desired upgrade [14,33,47]. However, thus far, the uptake evidence is mixed; Opiyo [48] observed many upgrades, which contrasted findings by Bisaga and Parikh [49]. Households may not upgrade due to the higher payments required or because their SHS and appliances sufficiently cover their needs [33].…”

Section: Upgrading Retaining and Switching Shsmentioning

confidence: 99%

Examining the Journey of a Pay-as-You-Go Solar Home System Customer: A Case Study of Rwanda

2021

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Solar home systems (SHSs) are successfully addressing energy access deficits across the globe, particularly when combined with pay-as-you-go (PAYG) payment models, allowing households to pay for energy services in small instalments. To increase energy access, it is vital to understand the PAYG SHS customer journey in depth. To aid this, the paper presents unique data from active customers, consisting of structured interviews (n = 100) and two focus groups (n = 24) across two districts in Rwanda. These results are presented under a novel customer journey framework, which describes all the individual stages a customer might experience, including awareness and understanding, purchase, usage, upgrade, recommendation and retaining or switching energy source. The paper reveals that the customer journey is non-linear and cyclical in nature, acknowledging that a household operates in a social network within which they could influence or be influenced by others. It also highlights the growing importance of SHS recommendations in raising awareness of SHSs, pointing to the shifts in the off-grid energy market environment where customer awareness no longer appears to be a main adoption barrier.

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“…Despite research on SHS usage rising over recent years, a number of gaps remain in the literature [44]. One of these is a lack of analysis on SHS usage patterns, with the limited studies on this subject often examining self-reported data, for instance in the Opiyo [45] study. Such self-reported data is normally recorded at seldom intervals or the respondent is asked to estimate an average, thereby making the data less precise.…”

Section: Gaps In Literaturementioning

confidence: 99%

Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access

et al. 2022

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Off-grid technologies, such as solar home systems (SHS), offer the opportunity to alleviate global energy poverty, providing a cost-effective alternative to an electricity grid connection. However, there is a paucity of high-quality SHS electricity usage data and thus a limited understanding of consumers’ past and future usage patterns. This study addresses this gap by providing a rare large-scale analysis of real-time energy consumption data for SHS customers (n = 63,299) in Rwanda. Our results show that 70% of SHS users’ electricity usage decreased a year after their SHS was installed. This paper is novel in its application of a three-dimensional convolutional neural network (CNN) architecture for electricity load forecasting using time series data. It also marks the first time a CNN was used to predict SHS customers’ electricity consumption. The model forecasts individual households’ usage 24 h and seven days ahead, as well as an average week across the next three months. The last scenario derived the best performance with a mean squared error of 0.369. SHS companies could use these predictions to offer a tailored service to customers, including providing feedback information on their likely future usage and expenditure. The CNN could also aid load balancing for SHS based microgrids.

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How basic access to electricity stimulates temporally increasing load demands by households in rural developing communities

Cited by 12 publications

References 8 publications

Rural Electrification Pathways: An Implementation of LEAP and GIS Tools in Mali

Rural Electrification Pathways: An Implementation of LEAP and GIS Tools in Mali

Examining the Journey of a Pay-as-You-Go Solar Home System Customer: A Case Study of Rwanda

Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access

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