A wind speed forecasting model based on multi-objective algorithm and interpretability learning

Li, Min; Yang, Yi; He, Zhaoshuang; Guo, Xiaoyi; Zhang, Ruisheng; Huang, Bingqing

doi:10.1016/j.energy.2023.126778

Cited by 24 publications

(3 citation statements)

References 54 publications

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“…However, it may obscure the variable interactions and heterogeneity shown only on some samples. ICE eliminates the influence of non-uniform effects and visualizes how the forecasting of the sample changes when the variable changes so that individual differences can be better observed [61]. In this study, both PDP and ICE plots were plotted together to better observe the average effect and individual sample variation, which are defined as:…”

Section: Effect Of Variables Evaluationmentioning

confidence: 99%

What Determinants Will Enhance or Constrain the Spatiality of Agricultural Products with Geographical Indications in Northeast China? An Interpretable Learning Approach

Luo,

Ma,

Wang

2023

IJGI

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Geographical indication (GI) offers a unique protection scheme to preserve high-quality agricultural products and support rural sustainability at the territorial level. However, not all the areas with traditional agricultural products are acknowledged with a GI. Quantifying the contribution of each factor to geographical indication agricultural products (GIAPs) can facilitate the formulation of effective policies to improve rural livelihoods. In this study, the random forest (RF) model was applied to investigate the contribution of multi-perspective factors, including nature, society, agriculture and market, on the distribution of GIAPs, and examined the driving causes using interpretable approaches. The empirical findings demonstrate that the RF model is able to accurately capture most of the important factors characterizing GIAPs and to make out-of-sample predictions of the study units which obtain GIs. This study revealed that natural conditions and market demand were contributing aspects to the disparity of GIAPs in Northeast China. The order of determinants was the category of online GIAPs (CatOn) > the number of online GIAPs (NumOn) > the area of black soil (BlaSoil) > the distance to offline stores selling GIAPs (DisOff). Of these, GIAPs was lower than ybase in parts of districts of Jilin and Heilongjiang Provinces when the area of black soil (BlaSoil) gradually increased. When the category and number of online GIAPs (CatOn and NumOn) were less than 20 and 5, respectively, GIAPs were enhanced, especially for 40% of the districts in Liaoning Province. Deepening understanding of GIAPs helps to better target and tailor sustainable development policies.

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Section: Effect Of Variables Evaluationmentioning

confidence: 99%

What Determinants Will Enhance or Constrain the Spatiality of Agricultural Products with Geographical Indications in Northeast China? An Interpretable Learning Approach

Luo,

Ma,

Wang

2023

IJGI

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“…However, the increase in interpretable components may generate more errors, resulting in a decrease in system performance. Therefore, the key to hybrid modelling is to explore the optimal solution to the "accuracy versus transparency trade-off" problem [36,37].…”

Section: Introductionmentioning

confidence: 99%

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Qin

Zhu

et al. 2023

Forests

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Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.

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“…With the rapid development of artificial intelligence technology, its application to time series wind speed forecasting has received considerable attention in terms of research and applications [16,17]. Machine learning, with its strong capability for nonlinear mapping, has demonstrated excellent performance in wind speed forecasting [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Forecasting Ultra-Short-Term Time Series Wind Speeds for Wind Turbines

Yang,

Pang,

Xiang

et al. 2023

Processes

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Accurate forecasting of ultra-short-term time series wind speeds (UTSWS) is important for improving the efficiency and safe and stable operation of wind turbines. To address this issue, this study proposes a VMD-AOA-GRU based method for UTSWS forecasting. The proposed method utilizes variational mode decomposition (VMD) to decompose the wind speed data into temporal mode components with different frequencies and effectively extract high-frequency wind speed features. The arithmetic optimization algorithm (AOA) is then employed to optimize the hyperparameters of the model of the gated recurrent unit (GRU), including the number of hidden neurons, training epochs, learning rate, learning rate decay period, and training data temporal length, thereby constructing a high-precision AOA-GRU forecasting model. The AOA-GRU forecasting model is trained and tested using different frequency temporal mode components obtained from the VMD, which achieves multi-step accurate forecasting of the UTSWS. The forecasting results of the GRU, VMD-GRU, VMD-AOA-GRU, LSTM, VMD-LSTM, PSO-ELM, VMD-PSO-ELM, PSO-BP, VMD-PSO-BP, PSO-LSSVM, VMD-PSO-LSSVM, ARIMA, and VMD-ARIMA are compared and analyzed. The calculation results show that the VMD algorithm can accurately mine the high-frequency components of the time series wind speed, which can effectively improve the forecasting accuracy of the forecasting model. In addition, optimizing the hyperparameters of the GRU model using the AOA can further improve the forecasting accuracy of the GRU model.

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A wind speed forecasting model based on multi-objective algorithm and interpretability learning

Cited by 24 publications

References 54 publications

What Determinants Will Enhance or Constrain the Spatiality of Agricultural Products with Geographical Indications in Northeast China? An Interpretable Learning Approach

What Determinants Will Enhance or Constrain the Spatiality of Agricultural Products with Geographical Indications in Northeast China? An Interpretable Learning Approach

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

A Novel Hybrid Deep Learning Model for Forecasting Ultra-Short-Term Time Series Wind Speeds for Wind Turbines

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