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Irradiance received on the earth’s surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1 min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image velocimetry (PIV) method. [ABSTRACT FROM AUTHOR]

For minute time scale solar photovoltaic (PV) power forecasting, the motion of clouds over PV power plant mainly contribute to the fluctuant and intermittent nature of solar PV power output. Therefore, research on cloud motion displacement (CMD) calculation to realize cloud motion prediction is a key sub-process for minute time scale solar PV power forecasting approaches. Fourier phase correlation theory (FPCT) is widely applied in CMD calculation for its superiority of simplicity and less computation, then an improved algorithm based on image-phase-shift-invariance (IPSI) is proposed to reduce the outlier probability of CMD results. However, at present, the current IPSI algorithm still has limitations and cannot avoid the occurrence of outliers altogether. In this paper, we presented a novel method, termed IPSI based multi-transform-fusion (MTF) method, to further improve the effectiveness compared with traditional FPCT and affine transform based IPSI method. First, three image transform methods satisfying IPSI condition, respectively wavelet transform (WT), affine transform (AT), and convolution transform (CT), are explored. Then the information increment of the transformed sky images using the above three methods is analyzed, respectively. Second, we determine the suitable image transform method for IPSI algorithm under specific cloud condition according to the corresponding information increment. Third, an IPSI based MTF method for CMD calculation in sky images is proposed. The original sky images are transformed through WT, AT, and CT to generate multiple images that maintain the same object motion information, then calculate the CMDs in each generated image. Finally, we apply Gaussian distribution to fit the multiple CMD values and taking its mathematical expectation as final CMD result. Various experimental results in 4 different scenarios show that the performance of the proposed approach is better than FPCT, AT based IPSI, and OF method, by reducing plenty of CMD outliers, thus delivering greater accuracy and robustness. [ABSTRACT FROM AUTHOR]

• The temporal distribution of irradiance is a mapping of cloud spatial distribution. • Irradiance data can be achieved using cloud position and RGB values in sky images. • Sky image-irradiance mapping model is updated according to current cloud conditions. • Minutely irradiance forecasting is achieved by a sky image-irradiance mapping model. Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red–green–blue (RGB) values and position information of pixels in sky images after background elimination and distortion rectification, to explore the mapping relationship between sky image and solar irradiance. Then a real-time sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Finally, the future solar irradiance within the time horizons varying from 1 min to 10 min ahead are capable to be forecasted by using the latest updated surface irradiance mapping model with extracted input from the current sky image. The average measures of proposed method by using MAPE, RMSE, MBE are 22.66%, 92.72, −1.26% for blocky clouds; 20.44%, 132.15, −1.06% for thin clouds and 18.82%, 120.78, −0.98% for thick clouds, thus deliver much higher forecasting accuracy than other benchmarks. [ABSTRACT FROM AUTHOR]

• Photovoltaic power presents volatility, annual periodicity, and adjacent similarity. • Deep learning and time correlation principles are combined in a forecasting problem. • Time correlation modification improves the accuracy of LSTM-RNN forecasting model. • Partial daily pattern prediction can be used to optimize the modification process. Photovoltaic (PV) power generation is an effective means to realize solar energy utilization. Due to the natural characteristics of random fluctuations in solar energy, the applications of PV power such as grid-connected PV power plant, distributed PVs, and building integrated PVs will introduce new characteristics to the generation and load side of the power grid. Therefore, accurate day-ahead PV power forecasting is of great significance for enabling grid manager to achieve PV power output data in advance and mitigate the influence of random fluctuations. To tackle the deficiencies of conventional artificial intelligence (AI) modeling methods such as overfitting problem and insufficient generalization ability to complex nonlinear modeling, a day-ahead PV power forecasting model assembled by fusing deep learning modeling and time correlation principles under a partial daily pattern prediction (PDPP) framework is proposed. First, an independent day-ahead PV power forecasting model based on long-short-term memory recurrent neural network (LSTM-RNN) is established. Second, a modification method is proposed to update the forecasting results of LSTM-RNN model based on time correlation principles regarding different patterns of PV power in the forecasting day. Third, a partial daily pattern prediction (PDPP) framework is proposed to provide accurate daily pattern prediction information of particular days, which is used to guide the modification parameters. Simulation results show that the proposed forecasting method with time correlation modification (TCM) is more accurate than the individual LSTM-RNN model, and the performance of the forecasting model can be further improved for those days with accurate daily pattern predictions under the proposed PDPP framework. [ABSTRACT FROM AUTHOR]