Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Zhang, Peng; Ma, Xin; She, Kun

doi:10.3390/su11215921

Cited by 8 publications

(3 citation statements)

References 72 publications

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“…In order to reduce the discreteness of the data, the component contents and mechanical properties of each sample were initialized according to Equation (3) [ 32 ].

where k refers to the number of rows.…”

Section: Methodsmentioning

confidence: 99%

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

et al. 2022

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During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of RC and RCV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application.

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“…In order to reduce the discreteness of the data, the component contents and mechanical properties of each sample were initialized according to Equation (3) [ 32 ].

where k refers to the number of rows.…”

Section: Methodsmentioning

confidence: 99%

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

et al. 2022

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“…Cheng et al [25] used an improved GM (1,N) model to simulate and predict China's clean energy consumption. Zhang et al [26] established a new incomplete gamma grey model (IGGM) to predict solar energy consumption in Japan. Wu et al [27] proposed a novel nonlinear grey Bernoulli model with fractional-order accumulation (FANGBM (1,1) model) to predict China's renewable energy consumption from 2016 to 2020.…”

Section: Introductionmentioning

confidence: 99%

Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders

Sun

Zhang²

2022

Sustainability

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The scientific prediction of energy consumption plays an essential role in grasping trends in energy consumption and optimizing energy structures. Energy consumption will be affected by many factors. In this paper, in order to improve the accuracy of the prediction model, the grey correlation analysis method is used to analyze the relevant factors. First, the factor with the largest correlation degree is selected, and then a new grey multivariable convolution prediction model with dual orders is established. Different fractional orders are used to accumulate the target data sequence and the influencing-factor data sequence, and the model is optimized by particle swarm optimization algorithm. The model is used to fit and test the energy consumption of Shanghai, Guizhou and Shandong provinces in China from 2011 to 2020 compared with other multivariable grey prediction models. Experimental results with the MAPE and RMSPE measurements show that our improved model is reasonable and effective in energy consumption prediction. At the same time, the model is applied to forecast the energy consumption of the three regions from 2021 to 2025, providing reliable information for future energy distribution.

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“…Under the pressure of environmental pollution and the power crisis, various international sites are devoted to developing sustainable electrical sources, such as hydro, wind, solar, geothermal, and bioenergy. A viable electricity strategy plays a critical role in energy generation [3]. The consumption of energy includes many factors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

2020

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The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.

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Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Cited by 8 publications

References 72 publications

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders

Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

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