Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted

doi:10.1016/j.energy.2016.11.061

Cited by 119 publications

(50 citation statements)

References 37 publications

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“…Considering the solar radiation's inherent complexity, which is influenced by many parameters, it is expected to complete data processing ahead [34,35]. Wavelet transform (WT) is considered to be the most commonly used data preprocessing method for decomposing time series and eliminating stochastic volatility.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Zhang

Wei

2019

Sustainability

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Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Zhang

Wei

2019

Sustainability

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“…The electrical output from solar resources is a major issue, particulary for island such as Guadeloupe Archipelago due to its non-interconnected electrical network. To improve the integration of this kind of energy in the electric network, in the preceeding work [1], a hybrid forecast model based on multiscale decomopsition methods, AR and NN models is proposed. Three multiscale decomposition methods have been tested (Empirical Mode Decomposition EMD, Ensemble Empirical Mode Decomposition EEMD and Wavelet Decomposition WD).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies and methods of variability characterization were carried out [11][12][13][14][15][16]. The paper is built as follows: section 2 describes the dataset considered and the data pre-processing, section 3 presents briefly the MHFM methodology (see [1] for a full description) and section 4 gives the metrics used to evaluate the MHFM performances. Section 5 describes the two strategies applied to determine the forecast horizon influence on the MHFM, the results obtained are presented in section 6.…”

Section: Introductionmentioning

confidence: 99%

Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model (MHFM)

Monjoly¹,

Calif²,

André³

et al. 2018

Preprint

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In this paper, the forecast horizon and solar variability influences on MHFM model based on multiscale decomposition, AR and NN models, are studied. This article follows the works published in [1] showing the performance of the MHFM using 3 multiscale decomposition methods and a forecast horizon equal to 1 hour. Several forecast horizon strategies and his influence on the MHFM performances are investigated. We show that the best strategy for a rRMSE variying from $4.43\%$ to $10.24\%$ is obtained for forecast horizons from $5$ minutes to $6$ hours. In a second part, the solar variability influence on the MHFM is studied. A classification based on a shows that the best performance of MHFM is obtained for clear sky days with a rRMSE of $2.91\%$ and worst for cloudy sky days with a rRMSE of $6.73\%$.

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“…In this context, hybrid theory-based models have been proposed based on the combination of different basic theories in not only short-term traffic flow forecasts, but forecasts in other industries as well. During recent years, Vlahogianni et al [10] have proposed an approach based on the genetic algorithm (GA), which has been coupled with ANN for short-term traffic flow prediction; Zhu and Zhang [25] have proposed a hybrid layered system, which has been made up of KARIMA with several neural networks; and Monjoly et al [26] have also proposed a global solar radiation prediction method based on multiscale decomposition methods. Considering the advantages of EMD which have been already discussed in Section 1.2, many hybrid approaches have been proposed with EMD as an irreplaceable choice, and have achieved relatively high accuracy in the forecast results.…”

Section: On Specific Models For Air Traffic Forecastingmentioning

confidence: 99%

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

Nai¹,

Liu

Wang³

et al. 2017

Algorithms

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Abstract:The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities.In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.

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Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

Cited by 119 publications

References 37 publications

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model (MHFM)

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

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