Forecasting China's electricity consumption using a new grey prediction model

Ding, Song; Hipel, Keith W.; Dang, Yaoguo

doi:10.1016/j.energy.2018.01.169

Cited by 244 publications

(115 citation statements)

References 43 publications

Supporting

Mentioning

114

Contrasting

Unclassified

Order By: Relevance

“…where u(t − 2) � 0, t < 2, 1, t ≥ 2, and f(t) � − 420.77848X (1) 2 (t)1.19678X (1) 2 (t) + 9020.93243. Subsequently, the visual comparison of original and forecasted OGMC (1,3) values is shown in Table 9. Table 9, notice that GM(1, 3) and GM(1, 3) produce inaccurate results with 42.55% and 68.51%MAPE values, respectively.…”

Section: Data Description and Model Calibrationmentioning

confidence: 99%

“…Grey system theory has gained extensive attentions from worldwide researchers and has been successfully used in many fields with favorable outcomes since it was designed by Deng in 1982 [1][2][3]. is theory is capable of addressing issues characterized by uncertainty, insufficient information, and limited data points, thereby providing strong technical support for uncertain analysis [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

Ding

2020

Complexity

Self Cite

View full text Add to dashboard Cite

Accurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain issues, instead of the incorrect multivariable grey model (GM (1, n)). However, the conventional approach to computing background values of the GMC (1, n) model is inaccurate, and this model's forecasting accuracy cannot be expected. ereby, the drawback analysis of the GMC (1, n) model is conducted with mathematical reasoning, which can explain why this model is inaccurate in some applications. In order to eliminate the drawbacks, a new optimized GMC (1, n), shorted for OGMC (1, n), is proposed, whose background values are calculated based on Simpson' rule that is able to efficiently approximate the integration of a function. Furthermore, its extended version that uses the Gaussian rule to discretize the convolution integral, abbreviated as OGMC G (1, n), is proposed to further enhance the model's forecasting ability. In general, these two optimized models have such advantages as simplified structure, consistent forecasting performance, and satisfactory efficiency. ree empirical studies are carried out for verifying the above advantages of the optimized model, compared with the conventional GMC (1, n), GMC G (1, n), GM (1, n), and DGM (1, n) models. Results show that the new background values can effectively be calculated based on Simpson's rule, and the optimized models significantly outperform other competing models in most cases.

show abstract

Section: Data Description and Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

Ding

2020

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…And recently it has also been extended to build the multivariate grey models, such as the DGM(1, N) [19], RDGM (1, n) [20], TDVGM(1, N) [21], etc. Thirdly, some other methods, such as the intelligent optimizers [22], kernel machine learning [23], data grouping [24], mega-trend-diffusion [25], have also been introduced to build the grey models.…”

Section: Introductionmentioning

confidence: 99%

The conformable fractional grey system model

et al. 2020

View full text Add to dashboard Cite

The fractional order grey models (FGM) have appealed considerable interest of research in recent years due to its higher effectiveness and flexibility than the conventional grey models and other prediction models. However, the definitions of the fractional order accumulation (FOA) and difference (FOD) is computationally complex, which leads to difficulties for the theoretical analysis and applications. In this paper, the new definition of the FOA are proposed based on the definitions of Conformable Fractional Derivative, which is called the Conformable Fractional Accumulation (CFA), along with its inverse operation, the Conformable Fractional Difference (CFD). Then the new Conformable Fractional Grey Model (CFGM) based on CFA and CFD is introduced with detailed modelling procedures. The feasibility and simplicity and the CFGM are shown in the numerical example. And the at last the comprehensive real-world case studies of natural gas production forecasting in 11 countries are presented, and results show that the CFGM is much more effective than the existing FGM model in the 165 subcases.

show abstract

“…The electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, the normal power system operation and power management. Besides, the prediction accuracy of electricity consumption directly determines the power construction, network planning and the planning of electricity marketing strategies [1,2,3,4]. Therefore, predicting the electricity consumption accurately is demanded and crucial to mineral companies.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, in [2], Song et al modified the gray prediction method and proposed a rolling gray prediction(NOGM(1,1)) model. [2] overcame the deficiencies of fixed structure and poor adaptability in the original gray prediction model. The empirical results showed the NOGM(1,1) model has higher prediction accuracy than original gray prediction model.…”

Section: Introductionmentioning

confidence: 99%

A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting

Zhang

2019

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management. However, electricity consumption prediction for the mineral company is difficult since electricity consumption can be affected by various factors. The problem is non-trivial due to three major challenges for traditional methods: insufficient training data, high computational cost and low prediction accuracy. To tackle these challenges, we firstly propose a Regressive Convolution Neural Network (RCNN) model, but RCNN still suffers from high computation overhead. Then we utilize RCNN to extract features from data and Regressive Support Vector Machine (SVR) trained with features to predict the electricity consumption. The experimental results show that RCNN-SVR model achieves higher accuracy than using the traditional RCNN or SVM alone. The MSE, MAPE, and CV-RMSE of RCNN-SVR model are 0.8564, 1.975%, and 0.0687% respectively, which illustrates the low predicting error rate of the proposed model.

show abstract

Forecasting China's electricity consumption using a new grey prediction model

Cited by 244 publications

References 43 publications

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

The conformable fractional grey system model

A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting

Contact Info

Product

Resources

About