An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Ni, Qiang; Zhuang, Shengxian; Sheng, Hanmin; Wang, Song; Xiao, Jian

doi:10.3390/en10101669

Cited by 20 publications

(14 citation statements)

References 37 publications

(54 reference statements)

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“…Step 3 Parameter initialization. The parameters of DBN model are initialized by the RBM using Equations (12)(13)(14).…”

Section: Preprocessing Of Data Setmentioning

confidence: 99%

“…Interval predication-based methods, as the name says, output an interval as the predication results to cover the future observations with a certain confidence level of expectation probability, which is more suitable to deal with uncertainties [13,14]. The upper and lower bounds in interval predication can not only highly cover the fallen objectives, but they also provide an accurate coverage probability as an indication, which obviously brings more quantitative information than point 2 of 18 prediction.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Interval Prediction Using a Deep Belief Network

et al. 2018

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In load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interval predication is one of the most effective. In this study, a deep belief network-based lower–upper bound estimation (LUBE) approach is proposed, and a genetic algorithm is applied to reinforce the search ability of the LUBE method, instead of simulated an annealing algorithm. The approach is applied to the short-term load prediction on some realistic electricity load data. To demonstrate the effectiveness and efficiency of the proposed method, it is compared with three state-of-the-art methods. Experimental results show that the proposed approach can significantly improve the predication accuracy.

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“…Step 3 Parameter initialization. The parameters of DBN model are initialized by the RBM using Equations (12)(13)(14).…”

Section: Preprocessing Of Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Load Interval Prediction Using a Deep Belief Network

et al. 2018

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“…According to [12], these systems will soon become one of the main components of electrical power plants and smart grids. Many researchers are open to this topic, such as the development of new MPPT algorithms to PV system arrays operating under partial shading and exhibiting multiple local maximum power points [13,14], appropriate updating of the current grid coded [15,16], modeling and analysis of utility-scale photovoltaic units in hybrid energy storage devices [12], and so on [17][18][19]. Finally, the hybrid systems take advantage of the appropriate mixing combination between the grid-connected configuration and the stand-alone architecture.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System

2018

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In the several last years, numerous Maximum Power Point Tracking (MPPT) methods for photovoltaic (PV) systems have been proposed. An MPPT strategy is necessary to ensure the maximum power efficiency provided to the load from a PV module that is subject to external environmental perturbations such as radiance, temperature and partial shading. In this paper, a new MPPT technique is presented. Our approach has the novelty that it is a MPPT algorithm with a dynamic hysteresis model incorporated. One of the most cited Maximum Power Point Tracking methods is the Perturb and Observer algorithm since it is easily implemented. A comparison between the approach presented in this paper and the known Perturb and Observer method is evaluated. Moreover, a new PV-system platform was properly designed by employing low cost electronics, which may serve as an academical platform for further research and developments. This platform is used to show that the proposed algorithm is more efficient than the standard Perturb and Observer method.

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“…To improve the algorithm, we can combine different prediction models to compensate for the shortcomings of each model and reduce the error of a single model [32,33]. For example, Wu J et al [34] combined the two basic models of the BP neural network and SVM, and introduced PSO and cross validation to optimize the parameters of the BP neural network and SVM, which effectively solved the problem of large prediction errors in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm

Niu

Dai

2018

Energies

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The continuous increase in energy consumption has made the potential of wind-power generation tremendous. However, the obvious intermittency and randomness of wind speed results in the fluctuation of the output power in a wind farm, seriously affecting the power quality. Therefore, the accurate prediction of wind power in advance can improve the ability of wind-power integration and enhance the reliability of the power system. In this paper, a model of wavelet decomposition (WD) and weighted random forest (WRF) optimized by the niche immune lion algorithm (NILA-WRF) is presented for ultra-short-term wind power prediction. Firstly, the original serials of wind speed and power are decomposed into several sub-serials by WD because the original serials have no obvious day characteristics. Then, the model parameters are set and the model trained with the sub-serials of wind speed and wind power decomposed. Finally, the WD-NILA-WRF model is used to predict the wind power of the relative sub-serials and the result is reconstructed to obtain the final prediction result. The WD-NILA-WRF model combines the advantage of each single model, which uses WD for signal de-noising, and uses the niche immune lion algorithm (NILA) to improve the model's optimization efficiency. In this paper, two empirical analyses are carried out to prove the accuracy of the model, and the experimental results verify the proposed model's validity and superiority compared with the back propagation neural network (BP neural network), support vector machine (SVM), RF and NILA-RF, indicating that the proposed method is superior in cases influenced by noise and unstable factors, and possesses an excellent generalization ability and robustness.

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An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Cited by 20 publications

References 37 publications

Short-Term Load Interval Prediction Using a Deep Belief Network

Short-Term Load Interval Prediction Using a Deep Belief Network

Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System

Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm

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