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Modelling Cooking Energy Demand and Stove Efficiency in Sub-Saharan Africa

Cooking is one of the most energy-intensive household activities in Sub-Saharan Africa (SSA), yet the estimation of cooking energy demand remains highly uncertain. This thesis aims to investigate how energy demand for cooking can be more accurately characterized and represented in clean cooking models. The work will identify and quantify the energy requirements of common cooking tasks and translate them into an adaptable framework that accounts for differences in stove and fuel efficiencies. The ultimate goal is to support more realistic and spatially explicit representations of cooking energy demand in tools such as OnStove.

Background

Understanding household energy demand for cooking is essential for designing effective clean cooking strategies and evaluating the performance of different technologies. However, energy demand is not uniform—it varies widely depending on what is being cooked, how it is cooked, and the technology used. For instance, boiling beans, deep frying, and heating water each require distinct thermal energy inputs, and stove performance and suitability can differ dramatically between fuels such as LPG, electricity, charcoal, and biomass.

Existing studies and databases provide useful information on the energy intensity and efficiency of various cooking tasks, yet these findings are often context-specific or based on laboratory tests. Moreover, most geospatial models of clean cooking, including OnStove, rely on simplified or aggregated demand assumptions. This limits their ability to capture the diversity of cooking practices and the implications for technology selection, energy supply, and emissions.

This thesis seeks to synthesize available knowledge on cooking energy demand and efficiency, and to explore how it can be integrated into geospatial and techno-economic modelling frameworks. The work will focus on translating empirical and literature-based data into a structured representation of energy demand that can vary by cooking task, stove type, and possibly geography.

Key research questions may include:

  • What are the main types of cooking tasks performed across Sub-Saharan Africa, and how do they differ in terms of energy requirements?

  • How do factors such as meal composition, cooking duration, and local culinary practices influence total household energy demand?

  • How do stove and fuel efficiencies vary across different cooking tasks and technologies (e.g. LPG, electricity, charcoal, improved biomass)?

  • What methodological approaches exist for estimating useful energy demand and accounting for stove-fuel specific performance?

Task Description

The student will begin by conducting a comprehensive literature review of cooking energy demand and stove efficiency across different cooking tasks commonly performed in SSA—such as deep frying, long boiling of legumes, shallow frying, flash frying, and water heating. The review will compile available quantitative data and identify key parameters.

Building on this synthesis, the student will design a methodological framework to estimate cooking energy demand under different technology scenarios. This may involve normalizing cooking tasks into energy-per-meal or energy-per-household metrics and linking these to stove efficiencies, final fuel energy demand, and utilization rates. The work will then explore how these parameters could be incorporated into OnStove or similar models—potentially through the definition of spatially variable or context-specific demand inputs.

Finally, the student will discuss the implications of improved demand characterization for modelling outcomes, such as total energy consumption, emissions, and cost-effectiveness of clean cooking solutions.

Learning Outcomes

  • Gain a detailed understanding of how cooking energy demand and stove efficiency interact.

  • Learn to critically assess and synthesize experimental and literature-based data.

  • Develop skills in translating empirical data into modelling parameters and frameworks.

  • Acquire experience in linking demand estimation with geospatial or techno-economic models.

  • Strengthen research and analytical skills relevant to energy access and clean cooking planning.

Prerequisities

This project welcomes students from energy systems, sustainable development, or related fields. An interest in household energy use, modelling, and data analysis is essential. Familiarity with Python, data processing, or GIS is an advantage but not required.

Duration

5–6 months, start January 2026.

Specialization track

Transformation of Energy System (TES) - Division of Energy Systems

Division/Department

Division of Energy Systems – Department of Energy Technology

How to apply

Send an email expressing your interest in the topic to Camilo Ramirez Gomez (camilorg@kth.se) and Manuel Enrique Salas (mess@kth.se).  

Supervisors

Camilo Ramirez Gomez
Camilo Ramirez Gomez postdoc camilorg@kth.se Profile
Manuel Enrique Salas Salazar
Manuel Enrique Salas Salazar doctoral student mess@kth.se Profile

Examiner

Francesco Fuso-Nerini
Francesco Fuso-Nerini associate professor francesco.fusonerini@energy.kth.se Profile

Key Literature

  1. Bharadwaj, B., Pullar, D., Seng To, L., & Leary, J. (2021). Why firewood? Exploring the co-benefits, socio-ecological interactions and indigenous knowledge surrounding cooking practice in rural Nepal. Energy Research & Social Science, 75, 101932. https://doi.org/10.1016/j.erss.2021.101932
  2. Fuso Nerini, F., Ray, C., & Boulkaid, Y. (2017). The cost of cooking a meal. The case of Nyeri County, Kenya. Environmental Research Letters, 12(6), 065007. https://doi.org/10.1088/1748-9326/aa6fd0
  3. IEA International Energy Agency. (2025). Universal Access to Clean Cooking in Africa (World Energy Outlook Special Report). Directorate of Sustainability, Technology and Outlooks. https://www.iea.org/reports/universal-access-to-clean-cooking-in-africa
  4. Khavari, B., Ramirez, C., Jeuland, M., & Fuso Nerini, F. (2023). A geospatial approach to understanding clean cooking challenges in sub-Saharan Africa. Nature Sustainability, 6(4), 447–457. https://doi.org/10.1038/s41893-022-01039-8
  5. Perros, T., Lisa Allison, A., Nabukwangwa, W., Mwitari, J., Kavuli, P., Chepkirui, W., Rosa, G., Shupler, M., Pope, D., & Puzzolo, E. (2024). Understanding drivers of fuel stacking among pay-as-you-go LPG customers in Nairobi, Kenya. World Development Perspectives, 35, 100622. https://doi.org/10.1016/j.wdp.2024.100622
  6. Scott, N., Leach, M., Clements, W., Scott, N., Leach, M., & Clements, W. (2024). Energy-Efficient Electric Cooking and Sustainable Energy Transitions. Energies, 17(13). https://doi.org/10.3390/en17133318
Page responsible:Sina Sheikholeslami
Belongs to: Energy Technology
Last changed: Nov 18, 2025
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