Data Standards for Integrated Energy Planning

The report outlines the critical role of geospatial least-cost modelling in achieving universal energy access, while highlighting significant data gaps in demand-side considerations, grid infrastructure, and clean cooking patterns. It argues for a shift from static to dynamic data inputs and suggests that integrating electrification and clean cooking planning can double the viability of electric cookstoves. The document provides frameworks for key data inputs and proposes cost-effective methods for filling gaps, such as AI-driven remote sensing and cell phone-based surveys.

• Geospatial least-cost modelling is essential for identifying efficient pathways to universal electrification, but its effectiveness is limited by data gaps and quality issues, particularly regarding granularity.
• Energy planning must transition from a supply-side focus to one that incorporates dynamic demand-side data, including willingness to pay, productive uses, and electricity growth patterns.
• Significant data gaps exist in electrification planning regarding low-voltage (LV) line locations, transformer/substation data, and the quality and reliability of grid electricity in rural areas.
• Clean cooking planning lags behind electrification in its use of geospatial tools due to complex cultural preferences, the practice of fuel stacking, and the low monetary cost of traditional biomass.
• Geospatial integrated planning for clean cooking can serve two primary roles: providing a public planning framework for stakeholders to evaluate trade-offs and offering market intelligence for technology-specific decision-making.
• Coordinating electrification and clean cooking planning can significantly increase the viability of electric cookstoves, potentially doubling it compared to independent planning.
• Advances in AI, remote sensing, and mobile technology offer cost-effective ways to fill data gaps, such as using satellite imagery for building footprints or cell phone surveys for grid reliability.
• Data quality for energy planning is defined by granularity, timeliness, accuracy, consistency, and completeness, but planners should be pragmatic and seek the minimum attributes required to generate useful insights.

APA

Sustainable Energy for All (2020). Data Standards for Integrated Energy Planning. https://www.seforall.org/system/files/2021-02/IEP-data-standards.pdf

Chicago

Sustainable Energy for All. Data Standards for Integrated Energy Planning. 2020. https://www.seforall.org/system/files/2021-02/IEP-data-standards.pdf.

Wikipedia

{{cite report |author=Sustainable Energy for All |title=Data Standards for Integrated Energy Planning |date=October 2020 |url=https://www.seforall.org/system/files/2021-02/IEP-data-standards.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{sustainableenergyforall2020data, author = {{Sustainable Energy for All}}, title = {{Data Standards for Integrated Energy Planning}}, institution = {Sustainable Energy for All}, year = {2020}, month = oct, url = {https://www.seforall.org/system/files/2021-02/IEP-data-standards.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }