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A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

机译：基于预测模型滤波和自适应变权确定的鲁棒加权组合预测方法

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摘要

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.
机译：中长期负荷预测在能源政策实施和电力部门投资决策中起着重要作用。为了提高年度电力负荷预测的鲁棒性和准确性，提出了一种基于预测模型滤波和自适应变权确定的鲁棒加权组合负荷预测方法。基于历史年份和预测年份之间的相似度，进行相似的选择年份。对预测模型进行过滤，以根据其综合有效性程度选择更好的模型。为了确定所选预测模型的自适应变量权重，将干扰变量引入免疫算法-粒子群算法（IA-PSO），并建立了粒子搜索速度的自适应可调策略。基于改进的IA-PSO确定的预测模型权重，获得了年度电力负荷的加权组合预测。案例研究表明了该方法的正确性和可行性。

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