Location Intelligence for
Renewable Energy Resources
Solution Strategist - Esri Indonesia

Session Highlights
Leveraging on location intelligence to uncover the potential of renewable energy resources
• Understanding the location and potential
• Leverage site suitability information
• Measure potential supply of a renewable energy resource (solar power case)
• Analyze variables predicting the potential of renewable energy resource (geothermal case)
• Monitor production and risk analysis in the project (geothermal case)
• Support stakeholders, break down silos, and enable efficient collaboration between teams

National Target Summary (Source: Kementerian ESDM Ditjen Energi Baru Terbarukan
dan Konservasi Energi - EBTKE)

Solar PV added to the
installed capacity:
170 MWh

Increase capacity for
energy transition:
Decrease in coal,
oil fuel and gas

Modeling Renewable Energy Potential Using GIS
Benefits of Renewable Energy
• Little to No Global Warming Emissions
• Improved Public Health and Environmental Quality
• A Vast and Inexhaustible Energy Supply
• Jobs and Other Economic Benefits
• Stable Energy Prices
• A More Reliable and Resilient Energy System
Union of Concerned Scientists: http://www.ucsusa.org/clean_energy/our-energy-choices/renewable-energy

1. Analysis on a local scale
2. Fine tuning of methodology
3. Broader analysis
Why GIS for Renewable Energy?
GIS Utilization for Modeling Renewable Energy

Modeling Solar Power Potential

Modeling Solar Power Potential
Leveraging on location intelligence to uncover the potential of solar power
Leverage site suitability information with data enrichment: e.g.
NASA solar radiation global dataset, Population, Purchasing
Power, POIs

Modeling Solar Power Potential
• Create solar radiation layer (output in Wh/m2)
• Identify suitable rooftops for solar panel based on variable criteria
with default tools (slope, minimum solar radiation, building
orientation, rooftop size)
• Calculate usable solar radiation per building (Zonal Statistics)
• Convert solar radiation to potential power, account for energy
conversion efficiency (15%) and installation’s performance ratio
(86%) - US EPA
Leveraging on location intelligence to uncover the potential of solar power

Correlation Matrix of Geothermal Variables

ArcGIS
Correlation Matrix of Geothermal Variables
Leveraging on location intelligence to analyze geothermal potential and energy production variables
• Enrich data with variables for analysis as attributes
• Call spatial and statistics libraries in R
• Calculate smoothed geothermal potential or energy
production rate in R (e.g. with Empirical Bayesian method)
and produce spatial output
• Execute hotspot analysis based on the rate calculated in R
• Create correlation matrix in R to evaluate attribute
relationships

Optimized hotspot analysis result

Correlation matrix result