Dataset opportunity
Smart Energies — 维护日志数据集机会
智能能源持有的中等规模维护日志数据集，可用于预测性维护和异常检测。
Score
80.6
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
56%
Action
采购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
全球预测性维护市场 = 2025年达到149.3亿美元，复合年增长率32.32% (2026-2035)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
Colorado co-op delivers 100% renewables in March, a first
utilitydive.com ↗ - 📰press2026-06-04
Les petites toitures solaires deviennent un produit comme les autres
greenunivers.com ↗ - 📰press2026-06-04
Les réseaux de gaz, hydrogène, chaleur et froid au menu du CSE
greenunivers.com ↗ - 📰press2026-06-04
Electric sector needs firm gas supply to protect grid reliability, gas industry report says
utilitydive.com ↗ - 📰press2026-06-04
Speed to power requires more transmission, not less competition
utilitydive.com ↗
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
资产经理监控太阳能发电厂的性能，这意味着内部数据分析。
source ↗
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 许可清晰
Buyer persona
工业AI和维护优化供应商
Smart Energies 拥有一个全面的维护日志数据集，主要以时间序列模式呈现，并富含来自各种能源工厂的地理数据、工业数据和物联网数据。这种丰富、细致的数据非常适合开发和完善预测性维护AI模型，从而能够在工业领域内预测设备故障并优化运营计划。多样化数据类型的结合使得能够全面了解资产的健康状况和随时间变化的性能。
全球预测性维护市场严重依赖此类数据，其价值在2025年约为149.3亿美元，预计到2035年将达到2457.3亿美元，显示出32.32%的强劲复合年增长率（CAGR）。尽管数据嵌入在运营系统中导致固有的访问复杂性，以及标准化来自不同工厂类型和位置的数据可能面临挑战，但对这种关键数据的高需求是由其提供的巨大商业价值驱动的，包括大幅成本降低（相对于被动维护可节省高达40%）和通过最大程度减少计划外停机时间来提高运营效率。⚠ 尽职调查（有价值的数据，可协商访问）：数据嵌入在能源工厂的运营系统中；标准化来自不同工厂类型和位置的数据可能存在潜在复杂性。· 企业：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Smart Energies 拥有超过650个运营中和在建的可再生能源工厂的广泛投资组合，为预测性维护提供了独特、专有的时间序列数据来源。该数据集为工业AI和维护优化供应商提供了一个无与伦比的机会，以开发和完善面向全球市场的解决方案，该市场预计到2025年将达到149.3亿美元。详细的运营数据和维护记录解锁了高级分析，在快速扩张的行业中提高了效率并减少了停机时间。这种高稀有度的数据正是当前市场中获取巨大价值所必需的。
See dimension details ↓- Dataset Specificity100
主导的“维护日志”，工业领域，4种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity94
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume58
4条证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value94
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
全球预测性维护市场，严重依赖维护日志数据进行AI/ML应用，预计在2026年至2031年间将以34.14%的复合年增长率增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4种证据类型，4条命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
所有权=自有，许可=清晰
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1个数据需求信号（1种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5个近期外部信号 — 超出已货币化范围的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit92
✓ 良好目标 — Smart Energies 是一家可再生能源生产商，拥有真实的运营业务，其副产品是生成有价值的维护日志和运营数据，其核心业务并非销售数据或情报。问题：不同来源报告的员工人数（从11-50到+100）和收入（60-80M欧元）存在一些差异，使其处于较高水平。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
此证据证实 Smart Energies 大量拥有并运营超过650个可再生能源工厂，生成了丰富的传感器数据流，这对于大规模资产监控和性能优化至关重要。
Industrial data
这突出了该集团在开发、建设和运营太阳能发电厂方面的端到端参与，提供了从实际资产获取工业运营数据的直接途径。
Maintenance logs
这直接证实了其维护团队存在详细记录，涵盖性能监控、预防性和纠正性维护以及故障排除，这对于预测性维护模型训练具有无价的价值。
Geospatial data
这明确了 Smart Energies 的主要欧洲运营足迹，包括法国、意大利、希腊和北欧国家等主要市场，为有针对性的AI解决方案提供了关键的地理背景。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for internal use in developing and deploying predictive maintenance models.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
High value driven by proprietary, real-time time-series maintenance logs from a large renewable energy plant portfolio, catering to the rapidly growing global predictive maintenance market.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Smart Energies Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.93 billion in 2025, CAGR 32.32% (2026-2035). Investment score 80.6/100 (confidence 0.56). Recommended action: Acquire.
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