基于分组灰色模型的电力消费建模与中期预测,GGM(1,1)

Vincent B. Getanda, P. Kihato, P. Hinga, H. Oya
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引用次数: 4

摘要

任何发展中部门的全球电力消费增长速度都快于预期,能源需求预测对于健全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势在必行。本文提出了分组灰色模型(GGM(1,1))对电力消费中期预测建模。将GGM(1,1)置于用电量数据场景下,验证其在时间序列数据预测中的能力和适用性。此外,通过实例分析,验证了数据分组技术在提高原灰色模型精度方面的作用。因此,数据分组技术提高了电力消耗预测的准确性。该模型可为未来电力生产和分配管理中的能源计划提高能源预测性能。此外,它还可以提高智能电网的效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electricity Consumption Modeling and Medium-Term Forecasting Based on Grouped Grey Model, GGM(1,1)
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.
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