Estimate and Stimulate Demand

From energypedia


Estimating present and future electricity demand of different customer segments is central to the design and implementation of sustainable electrification interventions. Demand assessment is a key input in technical and business model design, and the accuracy of its results directly affects the size and financial feasibility of the project[1][2]. This section will focus on electricity demand estimation and stimulation for rural settings, where it is especially hard to make accurate predictions as many communities have never had access to electricity. First, definitions of present, assessed and (future) effective demand and load profiles are provided. Second, guiding principles give contextual information on the various aspects that are important to consider to accurately determine electricity demand. Last, existing tools and resources in the area of demand estimation are listed and briefly explained.


Present electricity demand

The present electricity demand is the electrical energy consumed by all inhabitants at the time of the site visit. Where the community is not yet electrified, the present electricity demand is zero.

Assessed electricity demand

The assessed electricity demand is the amount of electricity that customers state they would use if there was electricity at this moment. It can be assessed by conducting surveys on site.

(Future) effective electricity demand

Effective electricity demand is the demand that is backed by financial resources and can actually be paid by the consumer. It is influenced by the willingness to pay (WTP) and ability to pay (ATP). The future effective electricity demand describes demand in future years which can be estimated by using socio-economic development factors. Assessing future demand and the growth potential is most challenging but very important as a mini-grid that can meet increased demand over time is more financially sustainable[3].

Electrical load profiles

The electrical load profile is the electrical load on a certain time axis, which varies according to customer type, temperature and seasonal effects. An average daily load profile gives insights on the peak load in kW which is required for the load forecasting and plant sizing and the energy demand in kWh to forecast demand and revenues. Aggregating the load profiles of all customers of the same class will result in the average daily load profile per consumer category and aggregating the load profiles of all consumer categories results in the average daily load profile in kW of the whole village.

Guiding Principles

Consider all factors that determine electricity demand

Figure 1 shows an overview of the various factors that have to be considered to most accurately estimate electricity demand. The community-based information can be assessed during a visit to the project location while the consumer-based information can be gained via on site surveys.

Fig. 1: Important factors to estimate demand (Own illustration based on Blechinger et al., 2016[1])

Adjust to local conditions and include the local community

To get first insights about current and future electricity demand for households or a community, it can be useful to take existing data from geographical and socio-economic comparable regions. However, for a more accurate demand assessment which is essential for a viable electrification intervention, an analysis of local conditions is inevitable. This goes hand in hand with including the local community and their needs. Users’ indications on their current electrical consumption and future needs are a key element in the design process of efficient off-grid systems. Involving locals at all project stages increases the chances of success for the project because satisfied users extend the viability of the system[1]. Hiring villagers who accompany the survey team during demand assessment enables the provision of local knowledge based input[3]. Another advantage of early stage inclusion is that local capacity building leads to increased skills on site and lowers the dependence on external know-how[1].

Efficient appliances are a relevant component in estimating demand

An increased level of complexity in demand estimation is the fact that off-grid solar products often do better and last longer when associated with efficient appliances[4]. This has important implications both for the demand estimates and the design of the business model. Developers should be aware of this uncertainty and integrate coping mechanisms in their business models. For example, the company CREEDS Energy raises awareness among its customers around energy efficient applications in order to increase the longevity and reliability of their SHS. Besides, the use of more efficient appliances can permanently reduce demand as less power is required for the same tasks[1].

Existing Tools and Resources

Name Nigeria specific Open source Description
PeopleSuN Survey Data Yes Yes The survey data within the PeopleSuN project comprise responses from 3,599 households and 1,122 enterprises. The survey included a set of questions specifically designed to ascertain the ownership of electrical appliances, as well as the timing of their usage during both the day and night. The dataset is publicly available on PeopleSuN Harvard dataverse for the open access of academic community, local stakeholders and international initiatives.
PeopleSuN Demand Modeling Yes Yes By utilizing the PeopleSuN Survey Data from 3,599 households and 1,122 enterprises, the PeopleSuN project team generated demand profiles based on appliance ownership and usage hours. For this, RAMP, an open-source bottom-up stochastic model for generating multi-energy load profiles was used. The calculated demand profiles are divided into two main categories: households and enterprises.
PeopleSuN Qualitative Data Yes Yes This qualitative study, conducted in rural and peri-urban Nigeria (North West, North Central, South South), aims to inform locally relevant electrification projects by gaining insights into energy needs and motivations, ensuring long-term success and sustainability.
MIT D-Lab Energy Assessment Toolkit (EAT) No Yes The tool aims to gather and analyse data about current energy access and expenditures, aspirational energy needs, existing supply chain and community institutions and stakeholders via a community-based assessment approach. It targets any organisation that is locally present in an off-grid region and guides through the process of information gathering and informed decision making on the most suited technologies and business models to meet the specific community needs. The toolkit includes a series of open source surveys, interview guides, and data analysis tools.
Community Energy Toolkit (COMET) No No COMET is a role-playing software tool built around a representation of a mini-grid system, intended to be used as an educational and collaborative planning tool in designing a community-sized mini-grid system. The tool is designed to be used within a process that explores mini-grid planning and operational decisions. After the simulation, one can generate reports and analyse data on consumer demand and payments[5].
GIZ Guide "What size shall it be?" No Yes The guide to mini-grid sizing and demand forecasting supports in assessing the electricity demand of a community and determining the appropriate size of a new solar mini-grid. The handbook is applicable to various geographical contexts where mini-grids can be implemented. Practical knowledge has been drawn from the authors’ experience in mini-grid implementation in sub-Saharan Africa.
Energy Access Explorer (EAE) Yes Yes The EAE is an interactive online platform that uses mapping to visualise the state of energy access in unserved and underserved areas. It analyses public data to make the connection between the demand and supply of energy so that users are able to identify and prioritise areas where energy markets can be expanded.
NOMAP database board Yes Yes As part of efforts to provide market intelligence to market actors in the off-grid energy space, NOMAP gathered data together with Fraym (a consumer data company) to identify viable communities for SHS deployment across ten states. The states analyzed include Abia, Anambra, Bauchi, Edo, Kaduna, Kano, Kwara, Nasarawa, Ondo, and Oyo states.
Fraym report 2018 by REA Yes Yes The report provides an overview of different mini-grid and solar home system customer profiles to help practitioners estimate residential demand and willingness to pay. The data cover main lighting sources, generator ownership, electricity appliance household assets and the overall average household spending as well as on- and off-grid energy. The report is based on the NOMAP database.
Study “Off - and weak-grid solar appliance market Nigeria” Yes Yes The study offers detailed insights in the markets of commonly used electricity appliances such as TVs, fans, refrigerators and solar water pumps. It informs on the common power type, product size, retail price, warranty and power consumption of each appliance sold in retail markets, as well as other findings relevant to sector stakeholders working in Nigeria.
Nigeria Integrated Energy Planning Tool Yes Yes The tool is an online, interactive data visualisation platform that was developed by fraym in cooperation with SEforALL, under its Universal Integrated Energy Planning programme. It aims to support Nigerian policy makers and practitioners making more informed decisions in order to improve the country’s energy access[6]. Once being registered, one can access and use the tool.
Nigeria SE4ALL Platform Yes Yes The platform aims to generate the most accurate data and latest tools to empower better electrification planning in Nigeria. Data and tools can be found for the three categories (i) Mini grids, (ii) Power sector and (iii) SHS and are continuously updated. For example, the SHS section includes the app “Rural Solar Home System market analysis” which enables practitioners to explore rural off-grid communities that may be economically viable locations of SHS companies. The video introduces these 4 new apps on Nigeria SE4ALL Platform V 3.0.

Open Smart Meter Yes Yes ENACCESS and First Electric created an open source GSM smart meter that meets IEC and key elements of the local regulations in Nigeria. The website includes hardware design, the web software as well as openly accessible firmware and directions, which are addressed to skilled personnal with software engineering knowledge. Smart meters are tracking the energy consumption of the customers and are therefore inevitable for developers.

Case Study: Green Business Area, Mali

This case study describes the Green Business Area, an approach to delivering solar off-grid electricity to local rural businesses while supporting their growth, developed by GERES. You can view and download the case study here.

Peoplesun Casestudy 3 GBA.pdf


  1. 1.0 1.1 1.2 1.3 1.4 Blechinger, P., Papadis, E., Baart, M., Telep, P., & Simonsen, F. (2016). What size shall it be? A guide to mini-grid sizing and demand forecasting. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.
  2. USAID. (2020). Key Steps in Mini-Grid Technical Design.
  3. 3.0 3.1 Energypedia. (2016). Demand Assessment for Mini-grids.
  4. Efficiency for Access. (2021). Off- and weak-grid solar appliance market Nigeria.
  5. Energy Action Partners. (2021). Energy Action Partners | COMET. Enactpartners.
  6. SEforALL. (2022). Introduction to the Nigeria Integrated Energy Planning Project.