Abstract
To solve the problem of relatively low efficiency in most of nowadays solar panels, this paper proposes a new solution by diagnosing and optimizing solar panel installations. Dr. Solar, a sensor-based device, acquires information on solar radiation, geographical position, inclination, roll, panel temperature, and ambient temperature and humidity. The sensor-based device is mounted to the solar panel for a few days while collecting data. The cloud-based service gathers information from the inverter while the device is moved to other installations. Combined with the data of power generation from the inverter, a cloud service is offered for processing and analyzing the data to maximize power production by adjusting the panel installation. During several short-term experiments in different places, Dr. Solar provided reliable data for analysis on the cloud platform, and a web interface was developed to calculate power production. The solution has increased photovoltaic panels’ efficiency by optimizing adjustments to the panel's physical position (tilt and angle). The paper describes the sensor-based device, the cloud service, and the algorithms used.
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Acknowledgments
This work was part of the project “Cloud-based analysis and diagnosis platform for photovoltaic (PV) prosumers” supported by the Manu Net scheme Grant number MNET20/NMCS-3779 and funded through the Research Council of Norway Grant number 322500, UEFISCDI – Executive Agency for Higher Education (Romania): Research, Development and Innovation Fund, contract no. 215/2020, and TÜBİTAK ARDEB 1071 (Turkey): Support Program for Increasing Capacity to Benefit from International Research Funds and Participation in International R&D Cooperation (Project No: 120N838).
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Berntzen, L., Teimourzadeh, S., Anghelita, P., Ming, Q., Ursu, V. (2023). A Sensor-Based Unit for Diagnostics and Optimization of Solar Panel Installations. In: Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-30694-5_9
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