A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Fu, Lei; Yang, Yiling; Yao, Xiaolong; Jiao, Xiangquan; Zhu, Tiantian

doi:10.3390/en12203817

Cited by 21 publications

(11 citation statements)

References 45 publications

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“…The model output was averaged to obtain the regional prediction results. In [95], sub regions were divided by EMD and hierarchical clustering. The representative power stations were selected based on the minimum redundancy maximum correlation (MRMC) criterion.…”

Section: B Forecasting Methods Of Regional Photovoltaic Power Stationsmentioning

confidence: 99%

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: B Forecasting Methods Of Regional Photovoltaic Power Stationsmentioning

confidence: 99%

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

Liu

et al. 2022

IEEE Access

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“…Neural networks have been widely used in recent years, particularly those that are convolutional [13][14][15]. In terms of image recognition [16][17][18][19], the research has focused on two different aspects. One focuses on cloud recognition as a means of predicting the output power of the PV module and the other on the recognition of hot spots or breaks in the panels through images captured by a drone or similar.…”

Section: Related Workmentioning

confidence: 99%

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

et al. 2021

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Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies.

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“…In this paper, after obtaining the final PV output forecasting value, the nominal mean absolute error (nMAE) and root mean square error (RMSE) are used to evaluate the forecasting accuracy [36], [37], as shown in (18) and (19), respectively.…”

Section: A Forecasting Accuracy Evaluationmentioning

confidence: 99%

A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

Xian

Yan

et al. 2020

Journal of Modern Power Systems and Clean Energy

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High-precision day-ahead short-term photovoltaic (PV) output forecasting is essential in PV integration to the smart distribution networks and multi-energy system, and provides the foundation for the security, stability, and economic operation of PV systems. This paper proposes a hybrid model based on principal component analysis, grey wolf optimization and generalized regression neural network (PCA-GWO-GRNN) for day-ahead short-term PV output forecasting, considering the features of multiple influencing factors and strong uncertainty. This paper first uses the PCA to reduce the dimension of meteorological features. Then, the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is realized. GRNN is used to regressively analyze the input features after dimension reduction, and the parameter of GRNN is optimized by using GWO, which has strong global searching ability and fast convergence. The proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead shortterm PV output forecasting, which is demonstrated in a case study on a real PV plant in Jiangsu province, China. The results have validated the accuracy and applicability of the proposed model in real scenarios. Index Terms-Photovoltaic output forecasting, principal component analysis (PCA), grey wolf optimization (GWO), generalized regression neural network (GRNN).

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A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Cited by 21 publications

References 45 publications

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

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