Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks

Gumar, Ali Kamil; Demir, Funda

doi:10.3390/en15228669

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…Machine Learning Tool Note [142] eXtreme Gradient Boosting, Light Gradient Boosting, MultiLayer Perceptron, Elman Neural Network, Long Short-Term Memory comparative study [143] MultiLayer As Table 2 illustrates, the forecast of PV generation is a well-known challenge with many proposed answers. Nonetheless, the accuracy of PV forecasts is still limited because the architecture of forecasting algorithms (including those applying AI) is built on inherently inaccurate weather forecasts.…”

Section: Sourcementioning

confidence: 99%

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

Orosz,

Rassõlkin,

Arsénio

et al. 2024

Energies

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The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems.

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Section: Sourcementioning

confidence: 99%

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

Orosz,

Rassõlkin,

Arsénio

et al. 2024

Energies

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show abstract

“…Dinçer and ˙Ilhan [29] comparatively employed feedforward-backpropagation artificial neural networks and KNN algorithms using temperature, humidity, pressure, and irradiance values to predict output power of PV panels. Gumar and Demir [30] utilized metaheuristic algorithms such as Genetic Algorithm (GA), PSO, and Artificial Bee Colony (ABC) in conjunction with an ANN model to predict solar energy outputs.…”

Section: Related Workmentioning

confidence: 99%

A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

Demir

2023

Applied Sciences

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The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.

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“…Dinçer and İlhan [27] comparatively employ feedforward-backpropagation artificial neural networks and KNN algorithms using temperature, humidity, pressure, and irradiance values for predicting output power of PV panels. Gumar and Demir [28] utilize metaheuristic algorithms such as Genetic Algorithm (GA), PSO, and Artificial Bee Colony (ABC) in conjunction with an ANN model to predict solar energy outputs.…”

Section: Related Workmentioning

confidence: 99%

A New Low-Cost IoT Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

Demir

2023

Preprint

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This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

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Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks

Cited by 11 publications

References 26 publications

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

A New Low-Cost IoT Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

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