A decade ago, renewable energy decisions were chained to static spreadsheets, sparse ground station measurements and months-long consultant loops. Today, any project stakeholder – from an analyst scouting land in Brazil to a control-room engineer fine-tuning reserves in Victoria – can pull a bankable, sub-hourly irradiance or wind time-series with a single application programming interface (API) call. That simple capability is quietly rewiring the energy industry’s economics:
- Developers unlock more sites, faster, with less risk.
- Financiers can quantify uncertainty, rather than smoothing it over.
- Operators isolate performance issues in minutes rather than quarters.
- Traders, energy management system (EMS) vendors, and system operators have live actuals and forecasts built into their dashboards.
Global decarbonisation schedules, volatile power markets, and rising capital costs are driving changes in the energy industry as renewable generation increases, and the pace of growth accelerates. In increasingly competitive markets, renewable developers, operators, and investors have little margin for error. The most effective operators are now making decisions that once relied on Excel spreadsheets and sparse measurements with high-fidelity, validated irradiance, and weather data APIs. Access to bankable actuals and power models and accurate forecasting data, such as those delivered through DNV’s Green Data products, deliver key inputs to digital tooling. Those that adopt these tools are reducing uncertainty, accelerating project timelines, and sharpening operational choices from planning to portfolio optimisation.
This article explores lessons from working with partners and customers across the energy industry. It is a guided tour of how access to trustworthy data is changing decision-making at four pivotal stages of the renewable asset lifecycle – and the impact those shifts are having to those organisations delivering on the energy transition.
Trust begins with the data
Every gigawatt of new renewable capacity must compete for limited capital and increasingly constrained grid infrastructure. In this environment, the differentiator is decision-grade environmental intelligence: knowing, not guessing, how much irradiance or wind a site will receive over its lifetime, in the next hour, or next five minutes. Irrespective of technology – photovoltaics, wind, hybrids, or battery storage – engineering and finance teams are starting to build their cases on detailed, bias-corrected resource models delivered through an API rather than a spreadsheet.
DNV, through their investment in green data products like Solcast, is translating more than 160 years of risk-management experience into cloud-native datasets, power models, and analytics that can be queried instantly and delivered digitally. Access to digitally native data and models is benefiting work across the lifecycle of a modern renewable asset – feasibility, design, monitoring, optimisation, and grid operation. At every decision point, innovation is driving progress by those who are already using high-fidelity weather and power-models in their day-to-day workflows and design their processes around those data.
Feasibility and site selection: Getting the map right
Speed and scale
Developers who once performed painstaking desktop studies on a handful of prospects each month now screen thousands of parcels and configurations. Scripts tap irradiance and wind APIs directly, feeding high-resolution terrain, albedo, and cloud-motion layers into automated layout engines; yield estimates return in minutes rather than weeks. That velocity produces a larger, better-ranked funnel – critical when auction portals open and competition is fierce. Legacy requirements for 8760 files, and static, clunky systems, serve to slow down the feasibility process, and force analysts to swap and manipulate data between systems.
Granular accuracy through variability analysis
Quality is keeping pace with speed. Contemporary workflows can combine typical meteorological year analyses with multi-decadal time-series and long-term averages so that analysts can quantify inter-annual variability and tighten P50 and P90 spreads even in a changing climate. Instead of assuming a static resource baseline, feasibility teams should be considering how factors such as El Niño frequency, aerosol loading, or storm-track drift are altering risk profiles. Bankable power models built for this level of detail, like DNV’s SolarFarmer, include local shading engines, 3D terrain losses, and sub-hourly resolution to reduce model bias over legacy hourly models, but need high-resolution irradiance inputs. DNV’s Solar Resource Compass uses the same SolarFarmer power model, and supports analysts to quickly select the most appropriate source of irradiance data, then implements DNV’s methodology for solar resource assessment. This gives developers results in minutes, supporting fast site-selection decision making, using investor-ready models and delivering the confidence to act from day one.
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Energy Global's Summer 2025 issue
Dive into the latest renewable energy insights in the Summer issue of Energy Global, out now! This edition features a guest comment from Change Rebellion on the role real change management can play in the global energy sector before a regional report, which looks at energy trends and transformations across the Americas. Other key topics are also explored, including offshore support vessels, floating wind, weather analysis, and battery storage. Contributors include Ørsted, CRC Evans, Miros, Solcast, and more, so don’t miss out!