2026 International Conference on Computational Science and Power Engineering (CSPE 2026)
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About this event
2026 International Conference on Computational Science and Power Engineering (CSPE 2026)
Date: July 15–18, 2026
Venue: Marina Bay Sands, Singapore
Event type: Academic & Industry Research, Power Engineering Innovations, Computational Modeling & Simulation, Sustainable Energy Solutions
Estimated attendance: 1,500+ attendees including researchers, engineers, academics, industry leaders, policymakers, and technology providers from 50+ countries.
Venue: Marina Bay Sands, Singapore
Event type: Academic & Industry Research, Power Engineering Innovations, Computational Modeling & Simulation, Sustainable Energy Solutions
Estimated attendance: 1,500+ attendees including researchers, engineers, academics, industry leaders, policymakers, and technology providers from 50+ countries.
1️⃣ Who attends (BUYERS / ATTENDEES)
CSPE 2026 attracts a hybrid audience of academic researchers, industrial engineers, and technology buyers. Key attendee segments include:
- Academic institutions (university professors, PhD researchers, lab directors)
- Power generation and distribution companies (utility firms, renewable energy providers)
- Industrial engineering teams (oil & gas, aerospace, automotive)
- Government agencies and policymakers focused on energy transitions
- Technology vendors (software for computational modeling, simulation tools, grid management systems)
- Consulting firms specializing in energy strategy and computational optimization
2️⃣ Location + attendee geographic origin
Show location: Marina Bay Sands, Singapore – a global hub for energy and technology innovation.
Attendee origin: Truly international with strong representation from:
- Asia-Pacific (China, Japan, South Korea, India)
- North America (USA, Canada)
- Europe (Germany, UK, France, Norway)
- Middle East (Saudi Arabia, UAE focused on energy transition)
3️⃣ Audience reach
Reach type: Global with deep technical specialization.
CSPE 2026 combines academic rigor (peer-reviewed papers) with industrial application (case studies from Siemens, ABB, etc.). While the core audience is technical professionals, the event’s location in Singapore and focus on sustainable energy ensures broad geographic diversity.
4️⃣ Sample buyer company names + websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Siemens Energy | siemens-energy.com | Head of Digitalization / Power Systems Engineering Manager | Key player in grid modernization and computational modeling for energy systems |
| 2 | BP Plc | bp.com | Renewable Energy Systems Analyst / Computational Modeling Lead | Investing heavily in AI-driven energy optimization and simulation tools |
| 3 | ABB Ltd | abb.com | Power Grids R&D Director / Digital Solutions Manager | Exhibitor/sponsor with needs in simulation software and grid analytics |
| 4 | Microsoft Azure | microsoft.com/azure | Industry Solutions Architect – Energy / HPC Product Manager | Cloud computing provider targeting computational science workloads |
| 5 | National Grid (UK) | nationalgrid.com | Grid Innovation Manager / Systems Engineering Lead | Seeking advanced simulation tools for energy transition planning |
| 6 | ExxonMobil Research | exxonmobil.com | Computational Science Manager / Digital Transformation Lead | Oil & gas firm modernizing with machine learning and modeling tools |
| 7 | ANSYS Inc | ansys.com | Director of Engineering Solutions – Energy / Academic Partnerships | Simulation software vendor with educational licensing needs |
| 8 | GE Renewable Energy | ge.com/renewables | Wind/Solar R&D Engineer / Digital Twins Program Lead | Needs computational tools for turbine design and grid integration |
| 9 | MIT Lincoln Laboratory | ll.mit.edu | Technical Director – Computational Sciences / Research Fellow | Government-affiliated R&D institution with high discretionary budgets |
| 10 | Saudi Aramco | saudiaramco.com | Upstream Digitalization Manager / Reservoir Simulation Lead | Middle Eastern energy giant modernizing with computational modeling |
| 11 | DNV GL | dnvgl.com | VP of Energy Transition / Computational Engineering Lead | Consulting firm advising on simulation standards and certification |
| 12 | Oracle Enterprise Labs | oracle.com | HPC Solutions Architect / Energy Industry Director | Technology provider targeting research institutions and utilities |
| 13 | Rolls-Royce Power Systems | rolls-royce.com | Simulation Software Procurement Lead / R&D Project Manager | Industrial manufacturer using computational models for engine design |
| 14 | U.S. Department of Energy | energy.gov | Program Manager – Computational Science / Grants Director | Funding body for academic and industrial research projects |
| 15 | Hitachi, Ltd. | hitachi.com | Energy Systems Innovation Lead / Computational Modeling Group | Japanese conglomerate integrating AI with power system simulations |
5️⃣ Job profiles, industries & event type
Best job profiles to target:
- Computational Modeling Managers
- Power Systems Engineers
- Research & Development Directors
- Grid Modernization Specialists
- Academic Deans – Engineering
- Energy Policy Advisors
- Industrial Software Procurement Leads
- Oil & Gas
- Renewable Energy
- Industrial Software
- Electric Utilities
- Government Research
- Academic Institutions
6️⃣ Estimated attendance / expected footfall
Total expected attendance: 1,500+ including:
- 800+ academic researchers and students
- 400+ industrial engineers and technical staff
- 200+ technology vendors and solution providers
- 150+ policymakers and government representatives
Event footprint: 15,000+ sqm exhibition space with 50+ demo zones for computational tools.
7️⃣ Key focus areas & buyer engagement
Key focus areas:
- AI/ML in power system optimization
- High-performance computing for energy modeling
- Grid resilience and smart grid simulations
- Renewable integration challenges
- Industrial applications of computational fluid dynamics (CFD)
- Faster simulation workflows
- Energy transition-ready tools
- Academic-industry collaboration platforms
- Compliance with evolving grid standards
8️⃣ Client-product fit note
To refine buyer targeting, please share your client’s website. For example:
- If selling HPC software: Prioritize Siemens, GE, DNV GL, and DOE
- If offering grid analytics: Target National Grid, ABB, Hitachi
- If focused on academic tools: Engage MIT, Stanford, and DOE grants teams
- If providing cloud solutions: Highlight Azure, Oracle, and AWS opportunities
9️⃣ Final recommendation
CSPE 2026 is highly recommended for targeting technical decision-makers in energy and computational fields. While academic attendees dominate numerically, the industrial and government buyers represent high-value prospects for:
- Software vendors (simulation, modeling, analytics)
- Cloud/HPC infrastructure providers
- Energy transition consultants
- Industrial equipment manufacturers modernizing with digital tools
Data sheet
2026 International Conference on Computational Science and Power Engineering (CSPE 2026) – Event Attendee & Buyer Profile Analysis
Event date: 13 November 2026 – 15 November 2026
Location: Hefei University of Technology, Hefei, Anhui, China
Event status: Upcoming
Research date: 29 June 2026
Event Overview
| Event Name | 2026 International Conference on Computational Science and Power Engineering (CSPE 2026) |
| Event Date | 13 November 2026 – 15 November 2026 |
| Event Status | Upcoming |
| Venue | Hefei University of Technology |
| City | Hefei |
| State / Region | Anhui |
| Country | China |
| Organizer | Organizer not publicly confirmed in the materials provided. |
| Official Event Website | Official website not publicly verified in the materials provided. |
| Event Type | Academic conference with industry-relevant research, engineering innovation, technical networking, and knowledge exchange. |
| Primary Category | Power & Energy |
| Secondary Applicable Categories | Science & Research; IT & Technology |
| Audience Reach | International conference positioning with strong China and Asia-Pacific relevance. Current-year attendee reach not yet publicly quantified. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low at this stage. No verified current-year organizer statistics were available in the materials provided. |
| Main Purpose of Event | To convene researchers, engineers, academic institutions, utilities, technology developers, and policy-relevant stakeholders around computational science methods and power engineering applications, including modeling, simulation, optimization, grid systems, and sustainable energy research. |
About the Event
CSPE 2026 appears to be an international technical conference focused on the intersection of computational science and power engineering. Based on the event title and the user-supplied details, the program is likely to center on applied research, engineering methods, simulation, optimization, grid systems, energy technologies, and scientific collaboration between academia and industry.
From a business development perspective, this is more relevant for high-value technical networking, university-industry collaboration, software and instrumentation outreach, engineering partnerships, and long-cycle enterprise or institutional sales than for mass-market footfall. The most valuable participants are likely to include faculty researchers, lab leaders, utility engineers, R&D teams, power systems specialists, industrial technology providers, and public-sector or policy stakeholders connected to energy transition and computational engineering.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University research leaders | Engineering universities, energy institutes, computational science labs | Influence research tool selection, software adoption, grant-linked procurement, and partnership decisions | High relevance for simulation software, HPC, measurement systems, lab equipment, and technical services |
| Power utility engineering teams | Grid operators, generation companies, transmission and distribution utilities | Evaluate grid optimization, modeling tools, digital engineering solutions, and reliability technologies | Strong fit for software vendors, monitoring providers, controls, sensors, and energy analytics firms |
| Industrial R&D and engineering teams | Equipment manufacturers, industrial automation firms, battery and energy technology developers | Assess applied research, component performance, design validation, and engineering methods | Relevant for engineering software, test systems, digital twins, and industrial consulting |
| Government and policy stakeholders | Energy administrations, science funding bodies, public labs, standards institutions | Influence research funding, demonstration projects, standards, and public-sector procurement directions | Important for public-sector positioning and future procurement mapping |
| Technology buyers and solution architects | Software firms, digital energy vendors, data platform providers, AI and analytics teams | Evaluate partnerships, interoperability, algorithm performance, and deployment opportunities | High relevance for co-selling, OEM partnerships, and technical integration opportunities |
| Procurement and sourcing teams | Universities, labs, utilities, large engineering enterprises | Support acquisition of software licenses, lab infrastructure, services, and equipment | More relevant in follow-up cycles than direct show-floor purchasing |
| Consultants and system integration advisors | Energy advisory firms, engineering consultants, digital transformation specialists | Recommend tools and influence specification decisions | Useful channel partners and referrers for specialized B2B offerings |
| Graduate researchers and technical contributors | PhD candidates, postdoctoral researchers, assistant professors, lab engineers | Strong technical evaluators, future institutional champions, paper authors, and pilot users | Valuable for early adoption, pilot projects, and technical credibility building |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Hefei | Host-city faculty, students, research labs, local engineering organizations | Medium | Strong academic and research concentration due to host-university setting |
| Anhui Province | Regional universities, public institutions, industrial engineering groups, provincial energy stakeholders | Medium to High | Likely source of nearby technical and institutional delegates |
| Yangtze River Delta and East China | Shanghai, Jiangsu, Zhejiang, and neighboring industrial and research hubs | High | Likely to contribute utility, manufacturer, and technology participants |
| National China market | Universities, state-owned enterprises, research institutes, power system organizations | High | Conference title suggests national draw beyond the host region |
| Asia-Pacific | Researchers and engineers from Japan, South Korea, Singapore, India, and Southeast Asia | Medium | Likely attendee profile based on international conference positioning; current-year country list not confirmed |
| Global academic and research community | Select overseas authors, speakers, and collaborators | Low to Medium | International in name, but actual non-Asia participation remains unverified at this stage |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is positioned as an international conference and is likely intended to attract authors, researchers, and technical stakeholders from multiple countries. |
| National / Asia-Pacific concentration | Secondary practical reach | For outreach planning, the strongest real-world concentration is likely to be China-first, then broader Asia-Pacific, unless a published country breakdown confirms wider international attendance. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Hefei University of Technology | Host academic institution | Named venue; highly relevant for conference hosting, faculty participation, labs, and technical collaboration | hfut.edu.cn | Dean, Professor, Lab Director, Research Center Director, Procurement Office Manager | Confirmed Government / Procurement Organization |
| State Grid Corporation of China | Utility / grid operator | Direct relevance to power systems, transmission, optimization, and digital grid engineering | sgcc.com.cn | Chief Engineer, Grid Technology Director, R&D Director, Procurement Manager, Digitalization Director | Strong Market Fit, Attendance Not Confirmed |
| China Southern Power Grid | Utility / grid operator | Relevant for computational modeling, grid stability, power dispatch, and energy digitalization | csg.cn | Power Systems Director, Innovation Manager, Procurement Director, Smart Grid Program Manager | Strong Market Fit, Attendance Not Confirmed |
| China Huaneng Group | Power generation enterprise | Relevant for generation optimization, simulation, and advanced energy systems research | chng.com.cn | Generation Technology Director, R&D Manager, Plant Digitalization Lead, Procurement Manager | Strong Market Fit, Attendance Not Confirmed |
| China Datang Corporation | Power generation enterprise | Potential buyer of optimization, monitoring, and engineering analytics solutions | china-cdt.com | Engineering Director, Plant Operations Director, Strategic Sourcing Manager, Energy Systems Lead | Strong Market Fit, Attendance Not Confirmed |
| China Energy Investment Corporation | Integrated energy enterprise | Relevant for advanced modeling, grid-edge technologies, power systems, and industrial energy analytics | chnenergy.com.cn | Innovation Director, Power Engineering Director, Procurement Manager, Data Analytics Lead | Strong Market Fit, Attendance Not Confirmed |
| Tsinghua University | Research university | Leading engineering and energy research institution with strong relevance to conference themes | tsinghua.edu.cn | Professor, Department Chair, Lab Director, Scientific Computing Lead | Strong Market Fit, Attendance Not Confirmed |
| Shanghai Jiao Tong University | Research university | Strong fit for computational science, energy systems, and academic-industry collaborations | sjtu.edu.cn | Professor, Research Institute Director, HPC Lab Manager, Procurement Administrator | Strong Market Fit, Attendance Not Confirmed |
| Xi'an Jiaotong University | Research university | Known relevance to power engineering and advanced engineering research domains | xjtu.edu.cn | Professor, Power Engineering Director, Research Center Manager, Lab Procurement Officer | Strong Market Fit, Attendance Not Confirmed |
| North China Electric Power University | Specialist academic institution | Direct thematic alignment with power engineering, energy systems, and electrical research | ncepu.edu.cn | Dean, Professor, Smart Grid Research Director, Power Systems Lab Head | Strong Market Fit, Attendance Not Confirmed |
Current-year attendee, exhibitor, sponsor, or speaker lists were not publicly verified in the materials provided. The table therefore combines one directly connected organization (host venue institution) with highly relevant buyer-side prospecting targets in China’s power engineering and research ecosystem. These organizations should be treated as outreach targets, not confirmed attendees, except where stated.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Research Director | R&D / Research | Director | Owns technical direction, collaboration priorities, and pilot adoption for research-driven solutions. |
| 2 | Professor / Principal Investigator | Academic / Research | Senior | Influences tool selection, publications, grants, and institutional partnerships. |
| 3 | Lab Director | Research Operations | Director | Key buyer/influencer for laboratory systems, simulation environments, and instrumentation. |
| 4 | Chief Engineer | Engineering | Executive / Senior | Relevant in utilities and industrial power organizations evaluating applied technical solutions. |
| 5 | Power Systems Director | Engineering / Operations | Director | Decision-maker for grid modeling, system optimization, and technical modernization. |
| 6 | R&D Manager | Research & Development | Manager | Good target for pilots, technical evaluations, and innovation projects. |
| 7 | Director of Procurement | Procurement | Director | Relevant for institutional, utility, or enterprise purchase execution. |
| 8 | Procurement Manager | Procurement | Manager | Important for follow-up when opportunities move from technical interest to acquisition. |
| 9 | CTO / Digitalization Director | Technology | Executive / Director | Relevant where computational science overlaps with software, analytics, HPC, and AI workflows. |
| 10 | Program Manager | Projects / Innovation | Manager | Useful contact for consortium projects, grants, and research-commercialization activity. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Utilities | Core fit for transmission, distribution, generation, and grid analytics stakeholders. | Grid optimization, reliability, planning, and digital power systems. |
| 2 | Research | Universities and research institutes are likely central participant groups. | Lab tools, software, grants, collaborations, and pilot studies. |
| 3 | Higher Education | Conference venue and likely attendee base support strong academic participation. | University procurement, faculty engagement, and scientific partnerships. |
| 4 | Electrical/Electronic Manufacturing | Applies to power equipment, controls, and testing stakeholders. | Component simulation, product validation, and power electronics engineering. |
| 5 | Mechanical or Industrial Engineering | Strong fit for applied engineering and industrial design optimization audiences. | Simulation, digital twins, test workflows, and engineering services. |
| 6 | Information Technology & Services | Relevant where computational science involves platforms, analytics, and HPC environments. | Scientific computing, cloud workflows, and data integration. |
| 7 | Computer Software | A strong match for modeling, simulation, optimization, and engineering analysis vendors. | CAE, grid simulation, optimization engines, and scientific software licensing. |
| 8 | Industrial Automation | Industrial energy and process optimization ties well to conference themes. | Controls, predictive analytics, and plant optimization. |
| 9 | Oil & Energy | Relevant for broader energy-sector engineering and asset optimization applications. | Plant modeling, forecasting, and performance analysis. |
| 10 | Government Administration | Public-sector science and energy bodies may participate or influence funding and standards. | Research grants, public procurement, and technical program partnerships. |
| 11 | Renewables & Environment | Strong thematic overlap where computational engineering supports clean energy systems. | Renewable integration, storage modeling, and environmental optimization. |
| 12 | Aviation & Aerospace | Secondary fit for advanced computational engineering users. | High-performance simulation and engineering modeling applications. |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No official attendance statistics verified in current materials | A previously supplied reference described 1,500+ attendees, but that reference conflicted with the confirmed location/date data and was not treated as verified for this edition. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No verified exhibitor prospectus or exhibitor list available | This appears more conference-style than expo-style, so exhibitor activity may be limited. |
| Buyer count | Not publicly confirmed | Unconfirmed | No verified buyer or delegate category statistics available | Best interpreted as a technical delegate audience rather than a pure procurement audience. |
| Speaker count | Not publicly confirmed | Unconfirmed | No current program agenda verified | Speaker organizations would materially improve account targeting once published. |
| Sponsor count | Not publicly confirmed | Unconfirmed | No sponsor page verified | Sponsors may include publishers, software vendors, and technical solution providers if announced later. |
| Historical attendance | Historical attendance not verified | Historical / prior-year evidence unavailable | No prior-year official archive reviewed in current materials | Additional verification is required before using any scale claims in sales outreach. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Computational modeling | Accurate simulation and validation of power systems and engineering processes | Software demos, benchmarking studies, technical workshops | Simulation platforms, CAE tools, scientific computing software |
| Power systems engineering | Grid planning, system reliability, load analysis, optimization | Utility and academic collaboration meetings | Grid analytics, monitoring, controls, digital twins |
| Sustainable energy solutions | Integration of renewables, storage, and low-carbon power strategies | Joint research and technology pilot discussions | Renewable integration software, storage optimization, energy forecasting |
| AI and data analytics | Improved prediction, optimization, anomaly detection, and automation | Show applied use cases tied to grid, plant, or lab outcomes | ML platforms, analytics services, HPC-enabled workflows |
| Research partnerships | Co-authorship, grants, applied testing, and commercialization pathways | University-industry meetings and sponsored research discussions | Research sponsorship, shared labs, pilot licensing |
| Digital transformation in energy | Modernization of operations, maintenance, and analytical workflows | Executive briefings and technical proof-of-concept offers | Data platforms, integration tools, cloud engineering, decision-support systems |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong for specialized technical, software, lab, research, utility, and engineering offerings. |
| Decision-maker availability | Medium | Senior technical influencers are likely present, but direct procurement authority may vary by organization. |
| Data collection potential | Medium | Useful for account identification and relationship mapping, but weak unless official attendee/speaker lists become available. |
| Apollo targeting potential | High | Clear industry and job-function targeting exists across utilities, universities, research, and engineering organizations. |
| Geographic targeting potential | High | China, East China, and Asia-Pacific are practical core geographies for targeted outreach. |
| Best outreach approach | High-value account-based outreach | Focus on technical credibility, pilot use cases, research collaboration, and engineering problem-solving rather than generic event list outreach. |
| Overall lead quality | High | Best for niche B2B solutions with technical value. Less suitable for broad consumer or commodity lead generation. |
| Best use case | Account prioritization and technical partnership sourcing | Useful for university-industry partnerships, utility innovation outreach, and scientific software prospecting. |
| Limitations / risks | Medium | Limited verified attendee data, likely slower buying cycles, and a potentially stronger academic than transactional procurement mix. |
Suitability for B2B attendee list building: Moderate. The event is strategically useful for high-quality technical account mapping, but current public evidence does not yet support a robust verified attendee-list build for the 2026 edition.
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Utilities; Research; Higher Education; Electrical/Electronic Manufacturing; Mechanical or Industrial Engineering; Information Technology & Services; Computer Software; Industrial Automation; Oil & Energy; Renewables & Environment; Government Administration | Build a focused account universe around research-driven and energy-engineering buyers. |
| Departments | Engineering; Research; Information Technology; Operations; Procurement; Program Management; Innovation | Prioritize technical evaluators and purchasing stakeholders. |
| Seniority | Director; VP; CXO; Head; Manager; Partner; Professor-equivalent where institution records exist | Reach budget owners, technical sponsors, and strategic collaborators. |
| Job titles | Research Director, Lab Director, Professor, Principal Investigator, Chief Engineer, Power Systems Director, R&D Manager, Grid Technology Director, Procurement Director, Procurement Manager, Digitalization Director, Program Manager, Innovation Director | Mirror the most likely conference buyer and influencer profiles. |
| Geography | China first; Anhui; Hefei; Shanghai; Jiangsu; Zhejiang; Beijing; Guangdong; broader Asia-Pacific for secondary outreach | Aligns with likely attendance concentration and practical prospecting radius. |
| Employee size | 201-500; 501-1,000; 1,001-5,000; 5,001-10,000; 10,001+ | Targets universities, institutes, utilities, and engineering enterprises with formal budgets. |
| Keywords | computational science, power engineering, smart grid, grid optimization, simulation, modeling, high performance computing, energy analytics, digital twin, renewable integration, power systems, scientific computing | Refines target accounts and contacts around conference-relevant themes. |
| Technologies | HPC, AI/ML platforms, engineering simulation tools, cloud computing, analytics infrastructure | Useful if selling software, compute infrastructure, or advanced data solutions. |
| Revenue range | Mid-market to enterprise; public institutions where revenue fields may be incomplete | Focuses on organizations capable of research, infrastructure, or enterprise-scale purchases. |
| Company type | Public sector, state-owned enterprise, university, research institute, private technology vendor, industrial manufacturer | Captures both direct buyers and high-value collaboration accounts. |
Suggested Apollo Search Logic: Use a China-first account set with title logic such as (“Research Director” OR “Lab Director” OR “Professor” OR “Principal Investigator” OR “Chief Engineer” OR “Power Systems Director” OR “R&D Manager” OR “Procurement Manager” OR “Digitalization Director”) AND company keywords such as (“power” OR “grid” OR “energy” OR “electric” OR “university” OR “research institute” OR “simulation” OR “smart grid”). Layer by industry and geography to separate academic targets from utility and industrial accounts.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-supplied event brief | Provided event input | Confirmed working event title, city, region, country, venue, and dates used in this report | Medium |
| Hefei University of Technology | Institution website | Venue institution identity | High |
| Reference description supplied in prompt | Conflicting descriptive material | Included a different date/location combination and an attendance estimate of 1,500+, therefore not treated as verified for the Hefei 2026 edition | Low for current-edition verification |
| Official event website / organizer page / agenda / attendee directory | Primary source check | Not publicly verified in the materials available for this report | Not available |
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