A classified irradiance forecast approach for solar PV prediction based on wavelet decomposition

Shi, Shenxing; Yan, Yuting; Lu, Hai; Zhen, Zhao; Wang, Fei; Ren, Hui; Li, Kangping; Mi, Zengqiang

doi:10.1109/naps.2016.7747957

“…It iterative partitioning minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. The squared Euclidean distance is used in this approach [33]. Compared with other clustering algorithms, it has the advantages of simple and efficient, low time and space complexity.…”

Section: ) K-means Clusteringmentioning

confidence: 99%

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Zhen

¹

,

Pang

²

,

Wang

³

et al. 2019

IEEE Trans. on Ind. Applicat.

Self Cite

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The motion of cloud over PV power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and PSO optimal weights-based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Secondly, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute is used for simulation, the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

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Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Wang

¹

,

Pang

²

,

Zhen

³

et al. 2018

2018 IEEE Industry Applications Society Annual Meeting (IAS)

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The motion of cloud over PV power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and PSO optimal weights-based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Secondly, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute is used for simulation, the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

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Big Data Storage for the Modeling of Historical Time Series Solar Irradiations

Ali

¹

,

Nor

²

,

Ibrahim

³

et al. 2018

Advances in Data Mining and Database Management

0

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This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for modeling the 15 years of solar weather data. Total Power Loss Index (TPLI) is formulated as the main objective function for the optimization problem and meanwhile bus voltage deviations and penetrations of the PV farms are calculated. To solve the optimization problem, this study adopts the Mixed Integer Optimization using Genetic Algorithm (MIOGA) technique. By considering different time varying voltage dependent load models, the proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired. From the results, it is revealed that compared to single PV farm, the integration of two PV farms reduced more energy loss and reduced the total size of PV farms. Big Data Analytics is found very effective for the storing, handling, processing and the visualizing of the weather Big Data.

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A classified irradiance forecast approach for solar PV prediction based on wavelet decomposition

Cited by 3 publications

References 11 publications

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Big Data Storage for the Modeling of Historical Time Series Solar Irradiations

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