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Electricity Consumption Modeling and Medium-Term Forecasting Based on Grouped Grey Model, GGM(1,1)

机译:基于分组灰色模型GGM(1,1)的用电量建模和中期预测

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Global electricity consumption in any developing sector is increasing faster than expected and energy demand forecasting is vital for sound-sustainable energy supply-demand management. Consequently, developing accurate electricity demand forecasting models is inevitable. In this paper we propose the Grouped Grey Model (GGM(1,1)) in modeling medium-term forecasting of electricity consumption. GGM(1,1) is subjected to electricity consumption data scenario to ascertain its ability and applicability in time series data forecasting. In addition, analysis of an empirical example validates data grouping techniques in improving the accuracy of the original grey model. Hence the accuracy of the prediction on electricity consumption is improved due to data grouping techniques. The proposed model can improve energy forecasting performance for future energy plans of management in producing and distributing power. Moreover, it can enhance smart grid benefits.
机译:任何发展中部门的全球用电量增长速度都快于预期,而能源需求预测对于良好的可持续能源供需管理至关重要。因此,开发准确的电力需求预测模型是不可避免的。在本文中,我们提出了用于对电力消耗的中期预测进行建模的分组灰色模型(GGM(1,1))。 GGM(1,1)受用电量数据情景的影响,以确定其在时间序列数据预测中的能力和适用性。此外,对一个经验示例的分析验证了数据分组技术在提高原始灰色模型的准确性方面的有效性。因此,由于数据分组技术,提高了用电量预测的准确性。所提出的模型可以提高未来发电和分配电力管理中能源计划的能源预测性能。而且,它可以增强智能电网的优势。

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