Prediction of short-term PV power output and uncertainty analysis

Liu, Luyao; Zhao, Yi; Chang, Dongliang; Xie, Jiyang; Ma, Zhanyu; Sun, Qiang; Yin, Hongyi; Wennersten, Ronald

doi:10.1016/j.apenergy.2018.06.112

Cited by 201 publications

(53 citation statements)

References 49 publications

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“…And GRNN also has a stronger nonlinear mapping ability and a flexible network structure with high fault tolerance [26]- [28]. As a result, GRNN is a suitable tool for forecasting the PV output with many factors and complex randomness [29].…”

Section: Grnn Based On Gwomentioning

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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Section: Grnn Based On Gwomentioning

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

View full text Add to dashboard Cite

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“…A combination of hidden Markov chain and Gaussian mixture method is presented in [23]. In [24], generalised regression neural network, ELM, Elman neural network, genetic algorithm, and non-parametric kernel density estimation (NKDE) are presented for PSTSGF. A composite approach that includes quantile regression, Bayesian approach, and Markov chain is presented in [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Afrasiabi

Mohammadi

Rastegar

et al. 2020

IET Generation, Transmission & Distribution

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“…Before estimating the parameters of the residual distributions, the data were grouped by the time of the day and deterministic forecasts were performed first. Then, the parameters σ t of distributions were estimated by (18) and (19) due to the mathematical meanings derived from MLE and their same formulas as two metrics, RMSE and MAE. To reduce fluctuations caused by day-ahead prediction errors of meteorological factors, our experiments chose observed values in both the proposed method and benchmark models to ensure a fair comparison.…”

Section: Numerical Experiments Settings and Comparison Algorithmsmentioning

confidence: 99%

Probabilistic solar irradiance forecasting via a deep learning‐based hybrid approach

Yongjun

et al. 2020

IEEJ Transactions Elec Engng

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Probabilistic solar irradiance forecasting has received widespread attention in recent years, as it provides more uncertainty information for the future photovoltaic generation. In this study, a hybrid probabilistic solar irradiance prediction method is proposed, which combines a deep recurrent neural network and residual modeling. Specifically, the long short-term memory-based point prediction using historical records and related features is applied to obtain deterministic forecasts. Next, these deterministic forecasts are employed as inputs to estimate the residual distributions. Furthermore, maximum likelihood estimation is utilized to compute the parameters of the residual distribution. Finally, the point prediction and residual distribution jointly generate the final probabilistic forecasting results. Compared with other deterministic and probabilistic forecasting models, the proposed method yields promising results on a publicly available dataset.

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Prediction of short-term PV power output and uncertainty analysis

Cited by 201 publications

References 49 publications

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

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

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Probabilistic solar irradiance forecasting via a deep learning‐based hybrid approach

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