Purpose

Electricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.

Design/methodology/approach

The novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.

Findings

Two cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.

Originality/value

A fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

中文翻译：

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一种新型的具有自适应结构的分数离散灰色模型及其在用电量预测中的应用

目的

电力消费预测因其对电力政策的重大影响而成为重要课题。由于各种不确定因素，不同情况下用电量的增长趋势是可变的。然而，传统的灰色模型基于固定的结构，有时无法匹配原始数据的趋势。因此，预测准确性会随着病例的变化而变化。为了提高模型的适应性和预测能力，本文提出了一种新型的变结构分数离散灰色模型。

设计/方法/方法

通过改变结构，可以将新模型视为同质或非同质指数预测模型。并根据原始数据的特点选择合适的结构。分数累积的引入增强了新模型的预测能力。并且相对分数阶r是通过简单而有效的数值迭代算法计算出来的。

发现

以江苏和福建的用电负荷和用电量为例，对新型灰色模型的预测精度进行评估。四种广泛使用的灰色模型、三种经典统计模型和多层人工神经网络模型进行了比较。结果表明，新的灰色模型在所有情况下都表现良好，优于对比的八种模型。

原创性/价值

针对传统灰色模型的固定结构与原始数据变化发展趋势之间的矛盾，提出了一种具有自适应结构的分数阶离散灰色模型。在应用中，新模型具有令人满意的适应性和预测精度。