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Global Solar Nowcasting Market Strategic Research Report

Global Solar Nowcasting Market Strategic Research Report
$3,500 USD
Market Research Reports
Strategic Research Report
Global Solar Nowcasting Market
$2.36B2025
6.3%CAGR
2032Forecast
Market Research Reports · Global
Market Research Reports Intelligence Series

By Type: Satellite-Based Nowcasting, Sky Imager-Based Nowcasting, Machine Learning-Based Nowcasting, Others

By Application: PV Power Station, Commercial, Residential, Others

Regional Forecast: Asia Pacific, Latin America, MEA, Europe, North America

Key Players: Solargis, IBM Research, Reuniwatt, DNV(Solcast), Open Climate Fix, NIWA, CSP Services, Vaisala Xweather, CalibSun, Fraunhofer ISE, Vaisala

Region: Global
Formats: PDF, Excel, Word & PowerPoint
Base year: 2025 · forecast to 2032
Length: 120 pages
Market size 2025
$2.36B
Billion USD
Forecast CAGR
6.3%
2025-2032
Forecast 2032
$3.6B
Projected
Gebieden
5
Asia Pacific · Latin America · MEA · Europe · North America

Overzicht

Scope of the Report

The global Solar Nowcasting market size is predicted to grow from US$ 2,357 million in 2025 to US$ 3,592 million in 2032; it is expected to grow at a CAGR of 6.3% from 2026 to 2032.

Solar nowcasting refers to the short-term prediction of solar energy generation, typically over minutes to a few hours. It involves using real-time data from weather satellites, ground-based sensors, and meteorological models to estimate the amount of solar radiation that will reach the Earth's surface. This is especially important for optimizing the performance of solar power plants and managing energy grids, as it helps anticipate fluctuations in solar energy due to changing weather conditions like cloud cover, atmospheric particles, and sun position. Accurate solar nowcasting ensures better integration of solar power into energy systems and enhances overall grid stability.

The global market for solar radiation nowcasting is experiencing robust growth, characterized by a competitive landscape populated by numerous players, diverse product types, and widespread applications. Based on satellite nowcasting utilizes satellite data to monitor and predict solar radiation; based on sky imager nowcasting relies on sky imagers to capture sky conditions for radiation prediction; based on machine learning nowcasting employs machine learning algorithms to analyze relevant data for more accurate forecasts. In addition, there are other product types such as those based on numerical weather prediction models.

Solar radiation nowcasting is mainly applied in photovoltaic power stations, which helps optimize the operation and management of power stations, improve power generation efficiency, and reduce costs. In addition, it is also used in commercial and residential sectors, such as helping commercial buildings and residential households reasonably arrange the use of solar energy.

Europe, especially countries like Germany, Italy, and Spain, was the main concentration of the global solar radiation nowcasting market. The US market is also growing strongly, with a relatively fragmented competitive landscape. As the largest economy in the world, the US has a strong demand for energy, and with the continuous improvement of the proportion of solar energy in the energy structure, the market demand for solar radiation nowcasting is also increasing.

The Asia-Pacific region is showing great potential. Countries such as China and Japan have seen a continuous increase in the demand for solar radiation nowcasting with the rapid development of the solar photovoltaic and solar thermal utilization sectors. In particular, China has the world's largest solar photovoltaic installed capacity, and the demand for solar radiation nowcasting is huge. The market scale is expected to grow rapidly in the future, and the important position of the Asia-Pacific region in the global market will become more prominent.

This report presents a comprehensive overview of the global Solar Nowcasting market, covering market size and forecast, segmentation by product type and application, competitive landscape, leading players and regional and country-level outlook.

Segment by Type

  • Satellite-Based Nowcasting
  • Sky Imager-Based Nowcasting
  • Machine Learning-Based Nowcasting
  • Others

Segment by Core Technical Principle

  • Physical Model-based Nowcasting
  • Data-driven Nowcasting
  • Hybrid Model-based Nowcasting

Segment by Prediction Time Scale

  • Ultra-short-term Nowcasting
  • Short-term Nowcasting
  • Medium-short-term Nowcasting

Segment by Application

  • PV Power Station
  • Commercial
  • Residential
  • Others

Who Can Use This Report?

This report is written for decision-makers who need a clear, data-backed view of the global Solar Nowcasting market:

  • Manufacturers, suppliers and solution providers benchmarking their position and planning product, capacity and go-to-market strategy
  • Distributors, channel partners and end users in PV Power Station, Commercial, Residential evaluating demand and sourcing options
  • Investors, financial analysts and consultants assessing growth opportunities, competitive dynamics and M&A potential
  • Government agencies, industry associations and research institutions tracking industry developments and policy impact

Market snapshot

Global Solar Nowcasting Market Strategic Research Report snapshot, 2025–2032

Source: Market Research Reports
Market size CAGR 6.3%
Regional growth momentum
Market share by segment
Key metrics
Base value
$2.36B
2025
Forecast
$3.6B
2032
CAGR
6.3%
2025–2032
Gebieden
5
global
Key companies
SolargisIBM ResearchReuniwattDNV(Solcast)Open Climate FixNIWACSP ServicesVaisala Xweather
© MarketResearchReports.comDisclaimer: The actual data may vary in the final report which undergoes verification check post order confirmation.

Segments covered in this report

By Type
Satellite-Based NowcastingSky Imager-Based NowcastingMachine Learning-Based NowcastingOthers
By Application
PV Power StationCommercialResidentialOthers

Table of contents

Click a chapter to expand
01Executive Summary
02Industry Overview & Forecast
  • 2.1.1 Market Definition and Scope
  • 2.1.2 Market Size and Growth Forecast
  • 2.1.3 Volume Analysis
  • 2.1.4 Segment Outlook by Type
  • 2.1.5 Segment Outlook by Application
  • 2.1.6 Regional Outlook
  • 2.1.7 Structural Developments Shaping the Forecast
  • 2.1.8 Forecast Risks and Sensitivities
03Market Segmentation by Type
  • 3.1 Market Segmentation by Type
  • 3.1.1 Market by Type Overview
  • 3.1.2 Satellite-Based Nowcasting
  • 3.1.3 Sky Imager-Based Nowcasting
  • 3.1.4 Machine Learning-Based Nowcasting
  • 3.1.5 Others
  • 3.1.6 Volume Analysis
04Market Segmentation by Application
  • 4.1 Market Segmentation by Application
  • 4.1.1 Market by Application Overview
  • 4.1.2 PV Power Station
  • 4.1.3 Commercial
  • 4.1.4 Residential
  • 4.1.5 Others
  • 4.1.6 Volume Analysis
05Regional Market Forecast
  • Asia Pacific
  • North America
  • Europe
  • Middle East & Africa
  • Latin America
06Country-Level Market Forecast
  • 6.1 Asia Pacific
  • 6.1.1 China
  • 6.1.2 Japan
  • 6.1.3 Korea
  • 6.1.4 Southeast Asia
  • 6.1.5 India
  • 6.1.6 Australia
  • 6.1.7 Rest of Asia Pacific
  • 6.2 North America
  • 6.2.1 United States
  • 6.2.2 Canada
  • 6.2.3 Mexico
  • 6.2.4 Rest of North America
  • 6.3 Europe
  • 6.3.1 Germany
  • 6.3.2 France
  • 6.3.3 UK
  • 6.3.4 Italy
  • 6.3.5 Russia
  • 6.3.6 Rest of Europe
  • 6.4 Middle East & Africa
  • 6.4.1 Egypt
  • 6.4.2 South Africa
  • 6.4.3 Israel
  • 6.4.4 Turkey
  • 6.4.5 GCC Countries
  • 6.4.6 Rest of Middle East & Africa
  • 6.5 Latin America
  • 6.5.1 Brazil
  • 6.5.2 Rest of Latin America
07Growth Drivers & Inhibitors
  • 7.1 Growth Drivers & Inhibitors
  • 7.1.1 Section Overview
  • 7.1.2 Growth Drivers
  • 7.1.3 Growth Inhibitors
  • 7.1.4 Driver and Inhibitor Impact Assessment
  • 7.1.5 Analyst Perspective
08Key Company Profiles
  • 8.1 Solargis
  • 8.1.1 Company Overview
  • 8.1.2 Key Products & Segments
  • 8.1.3 Financial Performance (2023–2025)
  • 8.1.4 Business Strategy
  • 8.1.5 SWOT Analysis
  • 8.1.6 Strategic Implications (2026–2032)
  • 8.2 IBM Research
  • 8.2.1 Company Overview
  • 8.2.2 Key Products & Segments
  • 8.2.3 Financial Performance (2023–2025)
  • 8.2.4 Business Strategy
  • 8.2.5 SWOT Analysis
  • 8.2.6 Strategic Implications (2026–2032)
  • 8.3 Reuniwatt
  • 8.3.1 Company Overview
  • 8.3.2 Key Products & Segments
  • 8.3.3 Financial Performance (2023–2025)
  • 8.3.4 Business Strategy
  • 8.3.5 SWOT Analysis
  • 8.3.6 Strategic Implications (2026–2032)
  • 8.4 DNV(Solcast)
  • 8.4.1 Company Overview
  • 8.4.2 Key Products & Segments
  • 8.4.3 Financial Performance (2023–2025)
  • 8.4.4 Business Strategy
  • 8.4.5 SWOT Analysis
  • 8.4.6 Strategic Implications (2026–2032)
  • 8.5 Open Climate Fix
  • 8.5.1 Company Overview
  • 8.5.2 Key Products & Segments
  • 8.5.3 Financial Performance (2023–2025)
  • 8.5.4 Business Strategy
  • 8.5.5 SWOT Analysis
  • 8.5.6 Strategic Implications (2026–2032)
  • 8.6 NIWA
  • 8.6.1 Company Overview
  • 8.6.2 Key Products & Segments
  • 8.6.3 Financial Performance (2023–2025)
  • 8.6.4 Business Strategy
  • 8.6.5 SWOT Analysis
  • 8.6.6 Strategic Implications (2026–2032)
  • 8.7 CSP Services
  • 8.7.1 Company Overview
  • 8.7.2 Key Products & Segments
  • 8.7.3 Financial Performance (2023–2025)
  • 8.7.4 Business Strategy
  • 8.7.5 SWOT Analysis
  • 8.7.6 Strategic Implications (2026–2032)
  • 8.8 Vaisala Xweather
  • 8.8.1 Company Overview
  • 8.8.2 Key Products & Segments
  • 8.8.3 Financial Performance (2023–2025)
  • 8.8.4 Business Strategy
  • 8.8.5 SWOT Analysis
  • 8.8.6 Strategic Implications (2026–2032)
  • 8.9 CalibSun
  • 8.9.1 Company Overview
  • 8.9.2 Key Products & Segments
  • 8.9.3 Financial Performance (2023–2025)
  • 8.9.4 Business Strategy
  • 8.9.5 SWOT Analysis
  • 8.9.6 Strategic Implications (2026–2032)
  • 8.10 Fraunhofer ISE
  • 8.10.1 Company Overview
  • 8.10.2 Key Products & Segments
  • 8.10.3 Financial Performance (2023–2025)
  • 8.10.4 Business Strategy
  • 8.10.5 SWOT Analysis
  • 8.10.6 Strategic Implications (2026–2032)
  • 8.11 Vaisala
  • 8.11.1 Company Overview
  • 8.11.2 Key Products & Segments
  • 8.11.3 Financial Performance (2023–2025)
  • 8.11.4 Business Strategy
  • 8.11.5 SWOT Analysis
  • 8.11.6 Strategic Implications (2026–2032)
09Competitive Landscape
  • 9.1 Competitive Landscape Overview
  • 9.2 Competitive Intensity Assessment
  • 9.3 Key Player Strategies & Positioning
  • 9.4 Competitive Dynamics & Strategic Outlook
  • 9.4.1 Emerging Competitive Threats
  • 9.4.2 Consolidation vs. Fragmentation Outlook
  • 9.4.3 Competitive Response Matrix
  • 9.4.4 Strategic Recommendations, 2026–2032
10Porter's Five Forces Analysis
  • 10.1 Threat of New Entrants
  • 10.2 Bargaining Power of Buyers
  • 10.3 Bargaining Power of Suppliers
  • 10.4 Threat of Substitutes
  • 10.5 Competitive Rivalry
11PESTLE Analysis
  • 11.1 Political
  • 11.2 Economic
  • 11.3 Social and Demographic
  • 11.4 Technological
  • 11.5 Legal and Regulatory
  • 11.6 Environmental
  • 11.7 Strategic Implications of the PESTLE Assessment
12SWOT Analysis
13Future Trends & Outlook
  • 13.1 Future Trends & Outlook
  • 13.1.1 Trend Summary and Commercial Maturity Assessment
  • 13.1.2 Technology and Innovation Trends
  • 13.1.3 Long-Term Market Outlook
  • 13.1.4 Investment & M&A Activity Outlook
  • 13.1.5 Overall Outlook Assessment

Frequently asked questions

What is the current global Solar Nowcasting market size?
The global Solar Nowcasting market is estimated at US$ 2.36 billion in 2025 (base year) and is projected to reach US$ 3.59 billion by 2032.
What growth rate is expected for the Solar Nowcasting market through 2032?
The market is expected to grow at a CAGR of 6.3% from 2026 to 2032, expanding from US$ 2.36 billion in 2025 to US$ 3.59 billion in 2032, roughly 1.5 times its base-year value.
How is Solar Nowcasting defined?
Solar nowcasting refers to the short-term prediction of solar energy generation, typically over minutes to a few hours. It involves using real-time data from weather satellites, ground-based sensors, and meteorological models to estimate the amount of solar radiation that will reach the Earth's surface. This is especially important for optimizing the performance of solar power plants and managing energy grids, as it helps anticipate fluctuations in solar energy due to changing weather conditions like cloud cover, atmospheric particles, and sun position.
What are the main segments of the Solar Nowcasting market by type?
By type, the market is segmented into Satellite-Based Nowcasting, Sky Imager-Based Nowcasting, Machine Learning-Based Nowcasting and Others.
Which applications drive demand in the Solar Nowcasting market?
Key applications covered include PV Power Station, Commercial, Residential and Others.
Who are the key players in the Solar Nowcasting market?
Key players profiled include Solargis, IBM Research, Reuniwatt, DNV(Solcast), Open Climate Fix, NIWA, CSP Services and Vaisala Xweather, among 11 companies covered in total.
Which regions and countries are covered for Solar Nowcasting?
The market is analysed across Asia Pacific, North America, Europe, Middle East & Africa and Latin America, with 20 country-level markets including China, Japan, United States, Canada, Germany, France, Egypt and South Africa.
Who should buy the Solar Nowcasting market report?
The report is intended for manufacturers and solution providers, distributors and end users in PV Power Station, Commercial and Residential, investors and consultants, and government or industry bodies who need market size, segmentation, competitive and regional data for the Solar Nowcasting market.
What license options are available for this report?
The report is available as a Single User License (US$ 3,500, one named user), a Site License (US$ 5,250, up to 10 users) and a Global / Corporate License (US$ 7,000, unlimited users), all delivered in PDF format.

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Systematic collection from 500+ verified sources including SEC filings, industry databases (Bloomberg, Statista, OECD), regulatory filings, trade publications, patent databases, and company annual reports. AI-assisted extraction identifies relevant data points across 10,000+ documents per report.

02
Market Sizing — Bottom-Up & Top-Down

Dual-validation approach: bottom-up sizing aggregates segment-level production, consumption, and trade data; top-down sizing cross-validates against macroeconomic indicators and total addressable market estimates. Discrepancies >5% trigger analyst review.

03
Competitive Intelligence

Company profiles built from public financial disclosures, product launches, M&A activity, job postings (as capability proxies), and supply chain mapping. Market share estimates triangulated across revenue, capacity, and shipment data.

04
Demand Forecasting

CAGR projections use time-series regression on 5-10 years of historical data, adjusted for identified demand drivers (technology adoption curves, regulatory catalysts, demographic shifts) and demand inhibitors (cost barriers, substitution risk). Scenario modeling covers base, optimistic, and conservative cases.

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