DOI: 10.5281/zenodo.17201991
title: Sensorization and Monitoring Architectures in AgriPV and FPV: Systematic Review Dataset (2014–2024)

description: Context

This dataset accompanies a systematic review of agrivoltaic (AgriPV) and floating photovoltaic (FPV) systems, focusing on two complementary layers:



Sensorization — what is measured and with which sensors, including calibration and installation practices;

Monitoring architecture — the operational setup that enables continuous, reliable measurements (power autonomy, data acquisition and time synchronization, and communications/telemetry).


The Excel file contains 246 peer-reviewed studies (journals or conferences) published between 2014 and 2024 that explicitly report on sensorization and/or monitoring architecture in AgriPV or FPV contexts. Information was extracted only when explicitly stated in the source documents.

The review followed PRISMA 2020 reporting guidance. Searches were performed in IEEE Xplore, Scopus, and Google Scholar, combining domain terms (AgriPV/FPV) with instrumentation/operations concepts. Representative queries (adapted per database syntax) included:



“floating photovoltaic” OR “floating PV” OR FPV AND (monitoring OR sensor* OR IoT OR “water quality”)

“floating photovoltaic” OR “floating PV” OR FPV AND (“case study” OR pilot)

agrivoltaic* OR “agriPV” OR “agri photovoltaic*” AND (sensor* OR monitoring OR IoT OR “soil quality”)

agrivoltaic* OR “agriPV” OR “agri photovoltaic*” OR “floating PV” OR FPV AND (“case study” OR pilot)


Inclusion: peer-reviewed papers (journal or conference) in English that explicitly report sensorization and/or monitoring architecture in AgriPV or FPV.Exclusion: off-topic; review/overview only; non-peer-reviewed (e.g., book chapters, white papers, preprints, opinion pieces); lacking sensor/monitoring details; non-English; unavailable full text.

From 3,506 initial records, exclusions were: off-topic (3,071), lacking sensor/monitoring detail (60), non-English (20), not peer-reviewed (106), and unavailable full text (3). The final sample includes 246 papers.

Dataset Description (columns)

Each row corresponds to one paper. The fields are:





Paper ID — internal identifier for cross-checking.




Title / Authors / Year — bibliographic metadata.




Publication Type — journal article or conference paper.




Study Type — experimental, simulation-based, or project design.




Geographical Location — country/region studied.




System Type — FPV, AgriPV, or hybrid.




System Scale — simulation, lab-based, pilot-scale, or full commercial deployment.




System Characteristics — installation features (e.g., tracking, float design, crop arrangements).




Focus — primary objective (e.g., energy production, crop performance, environmental impact, cost analysis).




Crop — crop types (AgriPV, where applicable).




Meteorological Monitoring — auto-logged variables and sensors (e.g., irradiance, air temperature, humidity, wind, precipitation).




Energy Performance Monitoring — auto-logged variables and sensors (e.g., PV power, module temperature).




Agricultural Monitoring — auto-logged variables and sensors (e.g., soil moisture/temperature, canopy light interception).




Water Monitoring — auto-logged variables and sensors (e.g., pH, turbidity, conductivity, dissolved oxygen, water temperature, algal activity).




Structural Integrity Monitoring — automated sensors (e.g., strain gauges, tilt meters, displacement sensors).




Manual Field Measurements — variables measured with handheld instruments or lab testing.




Automation and Control Mechanisms — e.g., real-time feedback, automated irrigation, inverter logic.




Sensor Accuracy and Calibration Methods — accuracy levels and calibration routines when stated.




IoT Integration — whether an IoT architecture is used.




AI/ML Use — whether AI/ML is used and for what (e.g., fault prediction, crop modeling).




Data Transmission Method — communications (e.g., wired, Wi-Fi, ZigBee, LoRaWAN).




Data Acquisition and Processing Methods — logging tools, processing pipelines, analysis platforms.




Power Requirements and Sensor Supply — e.g., battery, solar-powered, grid.




Frequency and Duration of Data Collection — temporal resolution and observation period.




Maintenance and Durability — reliability, access, durability under environmental stress, component failures.




Policy and Regulatory Considerations — legal/permitting/policy notes affecting sensor deployment.




Cost Considerations — whether monitoring costs are discussed.




Key Findings — main results.




Challenges Identified — technical/environmental/economic challenges.




Recommendations for Future Work — as stated by the paper.


DOI — digital object identifier. 



RQs — Research Question(s) addressed by the paper and defined as:




Number
Research Question


1
What are the impacts of the environment on the PV system?


2
How does the PV system perform in terms of energy generation and thermal behavior?


3
What are the impacts of the PV system on biological or ecological systems?


4
How stable and reliable is the physical structure of the system?


5
How can digital technologies (IoT, AI, UAVs) improve monitoring and automation?


6
Are AgriPV/FPV systems economically viable and aligned with current policy frameworks?


7
What are the best design and integration strategies for AgriPV/FPV systems?




 



Usage Notes





Data reproduce information explicitly reported by each source, when no information is provided fields are filled with NA.




Suitable for meta-analysis, benchmarking of monitoring practices, and evidence-based guidance on sensorization and architectures in AgriPV/FPV.




This dataset supports a research article prepared for submission based on the same systematic review.


Please cite the dataset as:




Pereira, S. (2025). Sensorization and Monitoring Architectures in AgriPV and FPV: Systematic Review Dataset (2014–2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17201991


Funding

This research was partly funded by the PRR Mobilizing Agendas, project Alliance for Energy Transition with grant agreement ID C644914747-00000023. The work is also funded by national funds through FCT—Fundação para a Ciência e Tecnologia, I.P., in the framework of the UID/6478—SOL4R Applied Research in Solar Energy for the Energy Transition.