Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting

Jiang, Ping; Li, Chen

doi:10.1016/j.measurement.2018.04.014

Cited by 42 publications

(10 citation statements)

References 57 publications

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“…wherein r is the random number generated by the standard normal distribution N(0, 1) . Equations (25) to (29) indicate that as the entropy value becomes smaller and smaller, the search width becomes smaller and smaller, the search accuracy would become finer and finer, and the search efficiency also increases. 3.…”

Section: Bp Artificial Neural Algorithm and Thinking Evolutionary Algmentioning

confidence: 99%

“…But it is not perfect. The biggest challenges are its large prediction bias and narrow scope of application 35 . The modellable residual tail segment can effectively correct GM to improve the prediction accuracy, but it strictly limited the conditions that at least more than 4 tail segments should have the same residual sign.…”

Section: Residual Gm Network Model Based On Bp-pemea Correctionmentioning

confidence: 99%

“…Although the residual grey model simulation is known for its high accuracy, it is set under restricted conditions, that is, the tail segment eligible for modeling is linear with the same symbol. However, in reality, most sequences are in wave form or highly non-linear [23][24][25] . It is impossible to construct grey models for them to use the method of the residual tail.…”

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confidence: 99%

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Research on residual GM optimization based on PEMEA-BP correction

Duan

Zhu

Wei

et al. 2020

Sci Rep

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With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.

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Section: Bp Artificial Neural Algorithm and Thinking Evolutionary Algmentioning

confidence: 99%

Section: Residual Gm Network Model Based On Bp-pemea Correctionmentioning

confidence: 99%

mentioning

confidence: 99%

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Research on residual GM optimization based on PEMEA-BP correction

Duan

Zhu

Wei

et al. 2020

Sci Rep

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“…For example, Liu et al [43] employed WPD (wavelet packet decomposition) and EMD to decompose the wind speed in turn to make the sequences more stable and then use ELM to make the prediction. Jiang et al [44] combined BA (bat algorithm), FA (firefly algorithm), and CS (cuckoo search) to optimize the weight coefficients of the three neural networks in order to obtain higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model

Sun

Gao

2019

Energies

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Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input–output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.

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“…Researchers then realized that the accuracy of a prediction result can be improved by giving different weights to different methods. Recently, several studies have assigned different weights to each time point of different methods (i.e., variable-weight combination forecasting) [41][42][43][44]; however, the determination of the weights is a major problem in these studies.…”

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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Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting

Cited by 42 publications

References 57 publications

Research on residual GM optimization based on PEMEA-BP correction

Research on residual GM optimization based on PEMEA-BP correction

Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model

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

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