Cloud Feature Extraction and Fluctuation Pattern Recognition Based Ultrashort-Term Regional PV Power Forecasting

Wang, Fei; Li, Jianan; Zhen, Zhao; Wang, Chao; Ren, Hui; Ma, Hui; Zhang, Wei; Huang, Lang

doi:10.1109/tia.2022.3186662

Cited by 29 publications

(8 citation statements)

References 58 publications

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“…In a study by Tsai et al [27], the authors compared multiple GHI forecasting methods, including the hybrid nowcasting presented in Hu et al [9]. The hybrid model taking cloud movement into account is shown to achieve comparable accuracy to other methods, as the mean average percentage error (MAPE) of the method was 7.8%, which is within the same order of magnitude as more complex methods, such as the CNN and LSTM combination network published in the study by Wang et al in [28]. They achieved a 4% relative mean average error (rMAE), while its resource requirements remained very low.…”

Section: Related Workmentioning

confidence: 89%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

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In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

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Section: Related Workmentioning

confidence: 89%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

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“…Therefore, the research on total DPV power forecasting is more meaningful for the safe and stable operation of the distribution network. A variety of methods have been proposed for regional PV power forecasting, which can be generally divided into (1) forecasting-accumulation methods, which first forecast the output power of each PV site in a region and then aggregate the forecasting results to obtain the regional PV output [17,18]; (2) accumulation-forecasting methods, which first aggregate the PV power of each site to obtain the total regional output, and then use the regional output as input to forecast the regional PV output [19]; (3) clustering-forecasting-accumulation methods, which first group the regional sites into several clusters according to certain rules, the sum of the power over each cluster is then obtained, and forecast the power for each cluster, finally aggregate the forecasting result of each cluster to get the regional power [20]. (4) Up-scaling methods, which first establish the mapping relationship between some representative sites and the total output of the entire region, so that the PV output of the entire region can be more accurately predicted only relying on the data of part sites [21,22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Here, let i = j, that is, the number of rows and columns remains the same, ranging from 2 to 14. Since not all subgrids have DPV sites, the numbers of the selected sites are i ∈ [4,8,14,19,25,33,39,50,56,67,78,86,98].…”

Section: The Influence Of Nodes' Numbermentioning

confidence: 99%

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Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

Wang,

Fu,

Zhang

et al. 2023

IET Generation Trans & Dist

Self Cite

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Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs’ adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset.

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“…Additionally, for more study about the prediction for PVs, refs. [78][79][80][81][82][83][84][85][86][87] can be studied.…”

Section: Pv Output Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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Cloud Feature Extraction and Fluctuation Pattern Recognition Based Ultrashort-Term Regional PV Power Forecasting

Cited by 29 publications

References 58 publications

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

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