Key Data Sources for Energy Modeling

Calling all power systems modellers! Are you looking at #LCOE? Calculating technology competitiveness? The data going into models is critically important, but often overlooked because it can be hard to find. You want cost and performance assumptions that are granular (specific to the country you are studying) and robust (harmonised across several sources to minimise bias). Let me introduce GNESTE – a new database with over 5,000 entries from 56 sources on the #cost and #performance of #electricity generators. It covers seven technologies: #coal, #gas, #hydro, #nuclear, #solar #PV, #wind, and #battery #energystorage (together >92% of global generation). And it covers the key financial metrics: #capex, #opex, #wacc, #lifetime, build time, fuel price, plus #efficiency. We describe the database in this new paper (free to read), and you can download the database in Excel format: https://lnkd.in/eSFKshjg New data are coming out all the time so we want to make this a live, collaborative project. All the data are hosted on GitHub, so we can work together to improve and expand this resource: https://lnkd.in/exgR5BZq

I dug into the source code of this viral map of global energy infrastructure. Here's what I found. ⬇️ Brian Bartholomew (mention not working for some reason) built OpenGridWorks an interactive map unifying the world's entire power grid into one free explorable platform. 120K+ power plants. 2.7M line-miles of transmission. 800K+ substations. The rendering stack uses MapLibre GL JS with PM Tiles meaning the entire map loads fast, runs client-side, and handles massive vector tile datasets without slowing down. Brian stitched together over a dozen public sources into a single unified layer: EIA Form 860m for US power plant data HIFLD for transmission and substation geometry Global Energy Monitor for global plant tracking OpenStreetMap for base infrastructure WRI Aqueduct for water stress context Epoch AI for compute and energy demand trends IM3/PNNL for climate-energy modeling PeeringDB and TeleGeography for data center and network mapping AESO, USGS, US Census, NREL/AFDC, ITU, and IRS energy community data Plus OpenGridWorks' own derived datasets tying it all together. It's a full geospatial data integration project built on public data alone. Amazing work showing the energy infrastructure that keeps the world (and yes, AI) moving. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing (link in comments)  📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

🏠⚡ Real-world smart meter data reveals how heat pumps, EVs, solar, and battery are reshaping electricity demand ⚡🏠 New analysis from Energy Systems Catapult's Living Lab shows how low-carbon technologies - solar, battery, EVs, and heat pumps - are fundamentally changing residential energy consumption patterns. Using smart meter data from hundreds of UK homes with different combinations of these technologies, my colleague Will Rowe uncovered the following patterns: 🚗 EVs: Demand shifting for time of use tariffs * Peak charging occurs between midnight-6am, showing consumers respond to time-of-use tariffs * Winter demand jumps 34% vs summer - critical for network planning during peak periods ♨️ Heat pumps: Flexible but weather-dependent * Two distinct daily peaks (3:30-6:30 and 12:30-15:30) indicate smart tariff optimisation * Summer consumption indicates ~75 litres hot water usage per household daily * Significant load-shifting capability suggests potential for demand response ☀️ Solar + batteries: Grid relief with seasonal patterns * Homes consistently show lower daily grid consumption across three seasons * Summer sees reduced overnight charging as solar-battery synergy maximises self-consumption * Clear evidence of energy arbitrage behaviour 🌆 The bigger picture:  Consumer behaviour demonstrates strong price responsiveness, but all technologies show pronounced seasonal variation. Winter represents the critical design case for network capacity planning. 🗞️ What this means:  As LCT adoption accelerates, understanding these real consumption patterns becomes essential for network reinforcement, generation planning, and designing future flexibility markets. Read the full analysis: https://lnkd.in/eDGhnjUm Want access to real-world energy data? The Living Lab's 5,000+ households are helping derisk clean energy innovation via sharing data and taking part in trials of new energy technologies. Contact our team via https://lnkd.in/ehQUnw2Y to discuss how we can help you. #EnergyTransition #HeatPumps #ElectricVehicles #SolarPower #NetZero #EnergyData #Decarbonisation

AFREC lates Publication on ( Key Africa Energy Statistics ) AFREC’s ‘Key Africa Energy Statistics’ (KAES) report, prepared with the active collaboration of African Union Member States. The data included were jointly collected from AU Member States and validated to ensure reliable and credible information for decision makers. This report highlights key trends from the updated African Energy Information System (AEIS). Reliable, timely, updated and harmonised energy statistics are fundamental to evidence-based policymaking, investment planning, monitoring and evaluation of Africa’s progress towards sustainable energy transitions. The African Energy Key Statistics 2025 publication provides a consolidated statistical overview of Africa’s 2023 energy system, covering energy supply, transformation and consumption over more than two decades. This publication supports the implementation of Agenda 2063, the African Continental Power Systems Master Plan, the African Energy Efficiency Strategy, and global commitments under SDG 7 of Agenda 2030 and the Paris Agreement. It is produced using official national data reported to AFREC and processed in line with the International Recommendations for Energy Statistics (IRES). Between 2010 and 2023, Africa’s energy system expanded significantly in response to population growth, economic transformation and rapid urbanization. Total primary energy supply (TPES) increased steadily, driven mainly by fossil fuels and traditional biomass, while electricity generation expanded rapidly. Key findings include: • Total primary energy supply grew continuously (38% increase) over the period, with biomass and oil dominating. • Biomass remains the dominant fuel in Sub-Saharan Africa. • Electricity generation more than doubled since 2010, led by hydropower, gas fired generation and emerging renewables. • Total final consumption increased across all sectors, particularly in transport, households and industry. • Africa remains a net exporter of crude oil and natural gas, while many countries remain net importers of refined petroleum products and electricity. AFREC remains committed to providing timely and reliable data to inform decisions and guide energy policies. The KAES 2025 report is part of this effort, providing a solid evidence base validated by Member States. We hope that this report will support your planning and decision-making efforts as we work together towards a sustainable energy future in Africa. United, we can overcome the challenges and build the Africa we want.

Building Simulation cover article Understanding HVAC system runtime of U.S. homes: An energy signature analysis using smart thermostat data Heating, ventilation, and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance. Similar homes in the same location can have varying runtime due to different factors. To understand such heterogeneity, this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the USA using data from ecobee smart thermostats. Two approaches were compared for the energy signature analysis: (1) using daily mean outdoor temperature and (2) using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint (delta T) as the independent variable. The best-fitting energy signature parameters (balance temperatures and slopes) for each house were estimated and statistically analyzed. The results revealed significant differences in balance temperatures and slopes across various climates and individual homes. Additionally, this study identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator (LASSO) regression. Incorporating delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint preferences. Moreover, the cooling slope analysis of this study highlights the significant impact of outdoor humidity levels, underscoring the need to include latent loads in building energy models.   Details of the research can be found at https://lnkd.in/gkCM-q3U The article is co-authored by You Jeong Kim, Alexander Waegel, Max Hakkarainen, Yun Kyu Yi & William Braham #DatadrivenModeling #HVAC #EnergySignature #SmartThermostatDataset #ecobeeDYD

New dataset for energy modellers and analysts! This week, Iain Staffell and I released a major new update to Renewables.ninja. Our country-aggregated wind and solar datasets (in Europe) and country-aggregated weather and demand datasets (globally) now extend to December 2024, based on revised and updated models. Looking ahead, we’re working on a much larger upgrade with global, rebuilt, next-generation data, which will be available later this year... And also stay tuned for other additions and improvements coming later this year. You can explore the update here, integrate it into your workflows, and let us know what you create: https://lnkd.in/d3V64nsR