基于统计-神经-计算智能混合方法的电力负荷预测

M. Gavrilas, O. Ivanov, G. Gavrilas
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引用次数: 6

摘要

提出了一种基于混合统计计算智能模型的中期负荷预测方法。任何有意规划不同的电力活动的实体(如输配电运营商、电力供应商或能源管理人员)都可以使用该方法。该方法生成下一年365天的每日负荷概况预测。该统计模型基于传统的回归模型预测年能耗、月能耗和日能耗,而基于Kohonen神经网络自组织模型的计算智能技术或启发式优化技术(即引力搜索算法)生成一周中每天和每周的典型负荷曲线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electricity load forecasting based on a mixed statistical-neural-computational intelligence approach
This paper presents a medium term load forecasting methodology based on a mixed statistical computational intelligence model. The methodology can be used by any entity (such as transmission and distribution operators, electricity suppliers or energy managers) interested in planning different activities with electricity. The methodology produces daily load profiles forecasts for all the 365 days of the next year. The statistical model predicts annual energy consumption and monthly and daily consumptions based on traditional regression models, while typical load profiles for each day of the week and every week of the year are produced using computational intelligence techniques based on self-organizing models with Kohonen neural networks or a heuristic optimization technique, namely the Gravitational Search Algorithm.
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