Dataset opportunity
Solarfields — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Solarfields, usable for Predictive Maintenance and Anomaly Detection.
Score
75.1
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
49%
Action
Acquire
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
Global Predictive Maintenance in the Energy Market to reach $2.81 billion in 2026, with a CAGR of 25.05% (2026-2031) (source: Mordor Intelligence).
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-07-16
Lauréat du dernier AO solaire sur bâtiment, Diméo Énergie ouvre son capital
greenunivers.com ↗ - 📰press2026-07-16
La modulation des EnR en hausse au premier semestre, celle du nucléaire baisse [RTE]
greenunivers.com ↗ - 📰press2026-07-16
En juin, les cleantech lèvent plus de 91 M€
greenunivers.com ↗ - 📰press2026-07-16
La plus grande usine de CSR de France démarre
greenunivers.com ↗ - 📰press2026-07-16
Les résultats des principaux producteurs d’énergie renouvelable en 2025
greenunivers.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.
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Solarfields holds a substantial Industrial Sensor Dataset composed of Time Series data from its 100+ solar parks. This data, generated by physical SCADA and IoT systems, includes granular `industrial_data`, `geo_data`, and `iot_data`, making it highly suitable for Predictive Maintenance models by providing detailed performance metrics from specific hardware brands for failure prediction and operational optimization.
The global market for Predictive Maintenance in the energy sector is estimated to reach $2.81 billion in 2026, with a projected CAGR of 25.05% through 2031. Despite the need for technical extraction from asset management platforms, the dataset's rarity and direct applicability to this high-growth market make it exceptionally valuable for AI buyers seeking to minimize downtime and improve energy asset efficiency. ⚠ Diligence (valuable data, access to negotiate): Data is generated by physical SCADA and IoT systems across 100+ solar parks; Technical extraction from asset management platforms required; Data includes proprietary performance metrics of specific hardware brands · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence confirms Solarfields possesses a substantial, proprietary dataset of industrial sensor readings from its extensive renewable energy operations. The collection features real-time time-series data from over 100 solar parks, large-scale battery storage systems, and correlated environmental factors. For AI vendors focused on predictive maintenance, this dataset is a rare asset for training and validating models that optimize asset performance and prevent failures, directly addressing a global energy market projected to reach $2.81 billion by 2026.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector industrial, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand92
AI buyer demand is extremely high, driven by the rapid expansion of the Predictive Maintenance in the energy market, which is projected to grow at a 25.05% CAGR.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
ownership=owned, licensing=clean
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 data-appetite signals (0 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 5 recent external signals — proprietary data beyond what's already monetised
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
✓ good target — The company, now named Novar, develops and operates large-scale solar parks in the Netherlands, making it a prime holder of valuable, dormant sensor data from its core business of electricity generation. Issues: The company rebranded from Solarfields to Novar in 2023 to reflect a broader scope including energy storage and smart grids. [1, 5, 6]; The company is a market leader in the Netherlands, potentially making it larger than a typical SME, though its employee count is under 250. [1, 2, 9]
- Deep Qualification90
✓ pass — Novar (formerly Solarfields) is a data holder; its core business is the development and management of energy assets, not the sale of data. The company possesses valuable industrial sensor time-series data from its solar parks, a plausible byproduct used for operational optimization and management.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The dataset includes granular time-series data from IoT sensors across more than 100 solar parks, capturing critical metrics like inverter status and panel efficiency essential for developing component-level failure prediction models.
Industrial data
It contains operational time-series data from large-scale battery storage systems, detailing charge/discharge cycles and thermal performance for AI models aimed at optimizing battery health and longevity.
Geospatial data
The collection is enriched with tabular environmental data that correlates site-specific conditions with energy output across diverse geographical locations, enabling the development of more accurate and context-aware predictive models.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Coverage
Scanned sources
Deliverable
Premium dataset report
Solarfields Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance in the Energy Market to reach $2.81 billion in 2026, with a CAGR of 25.05% (2026-2031) (source: Mordor Intelligence).. Investment score 75.1/100 (confidence 0.49). Recommended action: Acquire.
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