Intelligent solar panel monitoring system and shading detection using artificial neural networks

Abdallah, Fahad Saleh M.; Abdullah, Mohd Noor; Musirin, Ismail; Elshamy, Ahmed M.

doi:10.1016/j.egyr.2023.05.163

Cited by 19 publications

(3 citation statements)

References 16 publications

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“…Now, with the advent of wireless monitoring systems, the constraints imposed by environmental conditions have been significantly reduced, enabling faster real-time decision-making [76]. There is already a novel monitoring system that supports the use of Artificial Neural Network (ANN) technology to detect shading and other faults in photovoltaic panels (PV) [77], and an efficient monitoring and control system for solar photovoltaic modules has been developed that combines the use of a nonlinear maximum power-point tracking (MPPT) backstepping controller with a custom wireless sensor network (WSN) [78]. Also, the variations in energy yield are likely due to environmental changes, shading or faults.…”

Section: Building Integrated Photovoltaic Technologymentioning

confidence: 99%

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Application of Photovoltaic and Solar Thermal Technologies in Buildings: A Mini-Review

Xiao,

Lai,

Chen

et al. 2024

Coatings

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Buildings account for a significant proportion of total energy consumption. The integration of renewable energy sources is essential to reducing energy demand and achieve sustainable building design. The use of solar energy has great potential for promoting energy efficiency and reducing the environmental impact of energy consumption in buildings. This study examines the applications of photovoltaic and solar thermal technologies in the field of architecture, demonstrating the huge potential of solar energy in building applications. To ensure a fresh and thorough review, we examine literature that encompasses the advancements made in the utilization of solar energy in buildings over the past decade. The key factors to consider in this study are reliability, performance, cost and aesthetics in real applications of photovoltaic and solar thermal technologies in the field of architecture, which have a significant impact on people’s acceptance of solar energy technology. Recent developments in feasible and effective optimization solutions for solar energy technologies are summarized. Accurate and convenient simulation techniques are also summarized for reference. The results show that the rapid progress of BIPV systems is fueled by advancements in three crucial areas: enhancing solar cell and module efficiency, reducing manufacturing costs and achieving a competitive levelized cost of electricity. The results can provide researchers with a reference for understanding recent technological developments in the integration of solar energy into buildings.

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Section: Building Integrated Photovoltaic Technologymentioning

confidence: 99%

“…BIPV applications [77] A novel monitoring system which can accurately detect shading and other faults is developed.…”

Section: Region Of Interest Reference Description Focusmentioning

confidence: 99%

Application of Photovoltaic and Solar Thermal Technologies in Buildings: A Mini-Review

Xiao,

Lai,

Chen

et al. 2024

Coatings

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“…In [6], EI enables the integration of solar energy into the electrical grid and solves problems related to solar energy production. A remote monitoring system with an integrated artificial neural network (ANN) for detecting shading in photovoltaic panels is presented in [7]. Paper [8] presents a system that uses Siamese-twin neural networks for anomaly detection.…”

mentioning

confidence: 99%

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Dobrilovic,

Pekez,

Ognjenovic

et al. 2024

Applied Sciences

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The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

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Driving Sustainable Growth: Leveraging Digital Transformation in Photovoltaic Manufacturing

Ramirez-Peña,

Barba,

Otero-Mateo

et al. 2024

Lecture Notes in Information Systems and Organisation

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Intelligent solar panel monitoring system and shading detection using artificial neural networks

Cited by 19 publications

References 16 publications

Application of Photovoltaic and Solar Thermal Technologies in Buildings: A Mini-Review

Application of Photovoltaic and Solar Thermal Technologies in Buildings: A Mini-Review

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Driving Sustainable Growth: Leveraging Digital Transformation in Photovoltaic Manufacturing

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