Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques

Li, Fang‐Fang; Wang, Si-Ya; Wei, Jiahua

doi:10.1063/1.4999240

Cited by 36 publications

(17 citation statements)

References 29 publications

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“…Reference [88] also presented a prediction model developed by the combination of Empirical Model Decomposition (EMD) and ANN for long-term prediction of the intensity of solar irradiance. The formula used to find the standard deviation (SD) is given by Equation (27).…”

Section: Review Of Pv Power Forecasting With the Incorporation Of Nnmentioning

confidence: 99%

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Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

et al. 2019

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Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

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Section: Review Of Pv Power Forecasting With the Incorporation Of Nnmentioning

confidence: 99%

“…hist and pred historical and predicted results [88]. P, γ, η, S, ∆t and n stand for solar active power, amount of solar irradiance, efficiency, total area of PV modules, PV cell temperature's forecast error, and co-efficient of the temperature, respectively.…”

Section: Model Used For Power Production or Resource/power Forecastinmentioning

confidence: 99%

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

et al. 2019

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“…Among them, wavelet analysis has good time-frequency localization characteristics, but the decomposition effect depends on the choice of basic function and the self-adaptability is poor [23]. EMD has strong self-adaptability, but there are problems such as end-effects and over-enveloping [24]. LMD has fewer iterations and lighter end-effects.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

et al. 2018

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Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

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“…In recent years, numerous experts and scholars have applied decomposition-reconstruction prediction models to predict targets that are obvious and have related fluctuations, such as wind speed and PV power generation. Two commonly used methods are wavelet analysis [25][26][27][28] and empirical mode decomposition (EMD) [29][30][31]. Both methods can decompose the original waveform and are able to improve the prediction accuracy; however, both have drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

Wang

Sun

2018

Sustainability

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It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

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Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques

Cited by 36 publications

References 29 publications

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

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