A group of scientists in China have conducted an extensive study on the existing low-cost approaches to photovoltaic monitoring. They found that only 11 of 88 studies related to PV monitoring included machine learning. The researchers urge the scientific community to place more emphasis on lightweight machine learning solutions and smartphone-based integration.
Researchers from the American University of Iraq have conducted a systematic literature review on low-cost monitoring systems for photovoltaic (PV) installations, focusing on hardware, software and system integration, and highlighting the challenges and opportunities for the future of these systems.
“As solar energy adoption accelerates, especially in off-grid regions and underserved areas, the demand for low-cost yet reliable PV monitoring systems has become increasingly important. These systems are essential for ensuring performance, detecting faults and supporting long-term operational efficiency where commercial solutions are not feasible,” the team said. “This review examined core technologies that support low-cost data acquisition (DAQ), including microcontrollers, analog-to-digital converters (ADCs), communications modules, and software platforms, along with design considerations such as accuracy, scalability, power consumption, and user accessibility.”
The review followed four phases: identification, title screening, abstract screening, and full-text review. Of the 1,139 initial articles, only 88 studies met the inclusion criteria and were included in the final systematic review. According to the team, 2021 was the peak year for relevant studies, followed by 2019 and 2022.
The articles reviewed covered a wide range of topics. Some focused on sensors, including current and voltage sensors, radiation and temperature measurements, and IV curve tracers. Others explored hardware components such as microcontrollers, ADCs, and various communications interfaces. Software-related research includes commercial engineering platforms, open-source and microcontroller-based solutions, custom-developed software, and specialized analytical and visualization tools. Communications protocols were also systematically reviewed, covering wired, wireless, and hybrid approaches.
The researchers identified three key areas where significant progress has been made: the integration of the Internet of Things (IoT), the application of machine learning (ML) and DAQ-PV systems themselves. Regarding IoT, the team noted that such systems reduce wiring and maintenance costs while enabling predictive maintenance and smart energy management. ML applications were highlighted for their ability to improve optimization without the need for additional sensors. The researchers found that DAQ-PV applications are increasingly used in various PV setups to improve operational performance.
“The major research gaps fall into two categories: research practices and design limitations,” the team noted. “Many studies lacked testing under standard test conditions (STC), reported no uncertainty or life cycle metrics, and used limited PV specifications. Design gaps included low-resolution ADCs, missing environmental inputs, incomplete IV curves, internet dependency, limited user interfaces, and minimal integration of ML, which was only present in 11 of the studies reviewed.”
Despite these challenges, the scientists concluded that the field offers significant opportunities. “Future work should focus on edge computing, lightweight ML for embedded systems, modular and application-specific DAQs, smartphone integration and digital twin technologies. Expanded use of ML in PV monitoring has the potential to significantly improve system intelligence, scalability and affordability,” they stated.
The review was published in “A systematic review of low-cost photovoltaic monitoring systems: technologies, challenges and opportunities”, published in Renewable and sustainable energy assessments.
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