Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

Zhang, Wanqing; Lin, Zhe; Liu, Xiaolei

doi:10.1016/j.renene.2021.12.100

Cited by 147 publications

(47 citation statements)

References 31 publications

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“…Also, evaluating the agricultural drought status, which is done by famous indicators such as standardized precipitation-evapotranspiration index (SPEI) and Palmer drought severity index (PDSI), directly requires the monthly scale ET0 rate of the region. Data-driven models like stochastic and artificial intelligence methods are efficient approaches that have shown good performance in modeling and predicting hydrometeorological variables in recent years (Essam et al 6 ; Dehghanisanij et al 7 ; Elbeltagi et al 8 ; Azad et al 9 ; Zhang et al 10 ; Zarei et al 11 ; Graf and Aghelpour 12 ; Chen et al 13 ). In ET0 cases, Karbasi 14 have used AIs for ET0 forecasting in 1, 2, 3, 7, 10, 14, 18, 24, and 30 days lead times.…”

Section: Introductionmentioning

confidence: 99%

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Aghelpour

Varshavian

Pour

et al. 2022

Sci Rep

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Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

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Section: Introductionmentioning

confidence: 99%

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Aghelpour

Varshavian

Pour

et al. 2022

Sci Rep

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“…Better prediction results can be obtained by improving the existing model, including wavelet transform [9], RTS smoothing algorithm [10], genetic algorithm [11], etc. Additionally, the prediction effect of some combination methods is also better than that of a single statistical econometric model [12,13]. The statistical econometric models can predict well in many cases, but they are mostly used to describe linear structure sequences.…”

Section: Energy Forecasting Modelmentioning

confidence: 99%

Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting

2022

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It is of great significance to be able to accurately predict the time series of energy data. In this paper, based on the seasonal and nonlinear characteristics of monthly and quarterly energy time series, a new optimized fractional grey Holt–Winters model (NOFGHW) is proposed to improve the identification of the model by integrating the processing methods of the two characteristics. The model consists of three parts. Firstly, a new fractional periodic accumulation operator is proposed, which preserves the periodic fluctuation of data after accumulation. Secondly, the new operator is introduced into the Holt–Winters model to describe the seasonality of the sequence. Finally, the LBFGS algorithm is used to optimize the parameters of the model, which can deal with nonlinear characteristics in the sequence. Furthermore, in order to verify the superiority of the model in energy prediction, the new model is applied to two cases with different seasonal, different cycle, and different energy types, namely monthly crude oil production and quarterly industrial electricity consumption. The experimental results show that the new model can be used to predict monthly and quarterly energy time series, which is better than the OGHW, SNGBM, SARIMA, LSSVR, and BPNN models. Based on this, the new model demonstrates reliability in energy prediction.

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“…to receive more satisfied forecasting results, such as hybridizing grey catastronphe and random forest [1] ; hybridizing simulated annealing algorithm and genetic algorithm [6] ; and hybridizing wavelet transform and random forest [7] . For hybrid different models, to concentrate the worst disadvantage of each single model, which almost is its theoretical limitation, to integrate some additional process from other model into the conducting process, such as seasonal mechanism, by computing the seasonal index (SI) for each seasonal point in a dataset with seasonal period, then, calculating the forecasting value by considering SI [11][12][13] . The other hybrid model example is inspired from the concept of recurrent neural networks (RNNs) [14] and long-short term memory method [15][16][17] , which employing past information to capture more accurate data patterns to improve the forecasting results.…”

Section: As Journal Of Management Science and Engineeringmentioning

confidence: 99%

Optimization by Hybrid/Combined Artificial Intelligent Models

Hong

2022

j. of manag. sci. & eng. res.

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Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

Cited by 147 publications

References 31 publications

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting

Optimization by Hybrid/Combined Artificial Intelligent Models

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