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Household electricity demand forecasting using adaptive conditional density estimation

机译:使用自适应条件密度估计的家庭电力需求预测

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摘要

Large-scale deployment of advanced smart grid technologies bolsters load forecasting as a vital requirement for deregulated power systems. In this regard, providing an accurate short-term load forecasting (STLF) can facilitate demand response applications and real-time electricity dispatch. STLF is mainly influenced by meteorological conditions among which investigating the relationship between temperature and household total electricity consumption is notably important due to their strong correlation. Accordingly, in this paper, we estimate the total electricity consumption to explore the impact of temperature in terms of a non-linear relationship with electricity demand. We propose the adaptive conditional density estimation (ACDE) method on the basis of kernel density estimation (KDE) to enhance the load forecast accuracy. The aim of the suggested approach is to decompose and examine the mentioned relationship in the context of both temperature-related, and residual components of the total consumption. The performance of the model to forecast the electricity demand is evaluated using a comparison study. The results prove that an ACDE model can significantly improve the recognition capability of the temperature-related component of aggregated power. Finally, the efficacy of the ACDE method is examined via numerical analysis of real data. (C) 2017 Elsevier B.V. All rights reserved.
机译:先进智能电网技术的大规模部署将负荷预测作为对放松管制的电力系统的一项至关重要的要求。在这方面,提供准确的短期负荷预测(STLF)可以促进需求响应应用和实时电力调度。 STLF主要受气象条件的影响,其中调查温度与家庭总用电量之间的关系尤为重要,因为它们之间具有很强的相关性。因此,在本文中,我们根据与电力需求的非线性关系来估算总电力消耗,以探索温度的影响。我们在核密度估计(KDE)的基础上提出了自适应条件密度估计(ACDE)方法,以提高负荷预测的准确性。所建议的方法的目的是在与温度相关的总消耗量的剩余成分中分解和检查上述关系。使用比较研究评估了模型预测电力需求的性能。结果证明,ACDE模型可以显着提高聚集功率中温度相关分量的识别能力。最后,通过对实际数据进行数值分析来检验ACDE方法的有效性。 (C)2017 Elsevier B.V.保留所有权利。

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