A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network

Korkmaz, Deniz; Açıkgöz, Hakan; Yıldız, Ceyhun

doi:10.1080/15435075.2021.1875474

Cited by 31 publications

(19 citation statements)

References 37 publications

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“…The empirical formula is expressed in the form of an exponential function. The specific expression is shown in equation (8). The coefficient a represents the maximum radiation that can be predicted on a sunny day, and the coefficient b and c control the rate of approaching a as the temperature difference increases.…”

Section: ) Single-site Photovoltaic Forecasting Methods Based On Dete...mentioning

confidence: 99%

“…With the penetration of largescale solar energy resources into the power grid, how to ensure the effective consumption of variable and stochastic solar energy resources under the normal operation of the power system is an urgent problem. Reliable prediction of photovoltaic can provide important data support for power system operation, which is the basis and key to realize the large-scale consumption of solar energy resources [7,8]. Photovoltaic forecasting can help to adjust the scheduling plan timely and improve the efficiency of solar energy utilization.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

Liu

et al. 2022

IEEE Access

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Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.

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Section: ) Single-site Photovoltaic Forecasting Methods Based On Dete...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

Liu

et al. 2022

IEEE Access

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“…In the last decade, renewable energy sources have started to attract increasing interest, with the consumption of fossil fuels causing severe diseases and environmental pollution [1,2]. Among these renewable energy source types, one of the remarkable clean and reliable energy sources is photovoltaic (PV) based energy systems [3,4].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hotspots in Photovoltaic Modules with Deep Learning Methods

Açıkgöz¹,

Korkmaz

DANDIL

2022

Turkish Journal of Science and Technology

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Solar energy systems are increasing their capacity in the energy industry day by day by operating with higher efficiency in parallel with technological developments. The functional operation of photovoltaic (PV) module contributes greatly to the optimal performance of these systems. On the other hand, detection and classification of faults occurring in PV modules are of vital importance in the operation and maintenance of solar energy systems. In this study, the classification of hotspots, which is one of the most common faults in Photovoltaic (PV) modules, is carried out by deep learning methods. First, data augmentation is applied to the images in the training dataset to improve the classification performance. Then, pre-trained deep learning models namely AlexNet, GoogLeNet, ShuffleNet, SqueezeNet, ResNet-50, and MobileNet-v2 are compared on the same test dataset. According to the obtained experimental results, AlexNet has the best performance with an accuracy value of 98.65%, while ResNet-50 provides the worst result with 94.59%.

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“…One such control system currently popular in this field of research is the artificial intelligence (AI)-based deep neural network (DNN) structure which is an improved extension of ANNs. Due to these advantages, in [27], a deep convolutional neural network (CNN) structure is presented for PV power forecasting and a non-intrusive load monitoring is proposed via deep learning for residential microgrids [28]. For instance, [29] achieves voltage stabilization of the DC-DC converters with low ripples via the deep reinforcement learning technique.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

2021

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In a microgrid, photovoltaic (PV) systems are broadly preferred with energy storage systems (ESSs) that form small-sized direct current (DC) microgrids. They are also termed local grids i.e., DC nanogrids, which feed the local consumers to some extent in the next decades. Therefore, ESSs enable the DC nanogrids more flexible and stable by preserving the intermittent nature of renewables. Yet still, feeding local consumers smoothly with PV-battery-based systems is exceedingly a considerable theme. In this context, proper control of power electronics converters as the main carrier of the system is essential. Besides, the rise of PV applications challenges possible issues upon integrating the conventional grid. Emerging possible issues and the ways of dealing with them have been developing day by day. Thus, it is inevitable that innovative methods will be put into practice. To achieve this goal, the deep learning aidedsensorless control approach is adopted. To validate the proposed control method, the training phase is presented elaborately with the help of the experimental setup of a DC nanogrid. From the obtained results, it is concluded that the deep learning-based approach reaches very small error values, captures the system dynamics with a high accuracy rate successfully, and allows the possibility to apply practically.INDEX TERMS Deep neural network (DNN), sensorless control, deep supervised learning, photovoltaics, power electronic converters, DC microgrid, DC nanogrid.

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A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network

Cited by 31 publications

References 37 publications

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

Classification of Hotspots in Photovoltaic Modules with Deep Learning Methods

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

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