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Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model

机译：考虑通过混合模型对空气污染防治政策影响的短期电负荷

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Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.
机译：自2013年以来，在中国颁布了一系列空气污染防治（APPC）措施，以减少空气污染水平，这可以通过改变电力消耗的动力消耗的行为来影响区域短期电力需求。本文分析了APPC措施的政策制度及其对区域短期电力需求的影响，并确定考虑到APPC措施的影响的区域短期负荷影响因素。在此基础上，本文提出了一种基于最佳和最差方法和灰色关系分析（BWM-GRA）的类似日选择方法，以构建训练样本集，这考虑了每日特征指标影响程度的差异电力负载。此外，开发了基于SALP群算法（SSA）优化的最小二乘支持向量机（LSSVM）的短期负荷预测方法。通过预测受中国北方空气污染影响的城市的负荷，并将结果与​​几种选定的模型进行比较，揭示了APPC措施对区域短期负荷的影响是显着的。此外，通过考虑APPC措施的影响和避免模型参数设置的主观性，所提出的负载预测模型可以提高准确性，并提供短期负荷预测的有效工具。最后，讨论了本文的一些局限性。

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