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首页> 外文期刊>Applied stochastic models in business and industry >Rejoinder to the discussion of 'Short-term forecasting of the daily load curve for residential electricity usage in the smart grid'
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Rejoinder to the discussion of 'Short-term forecasting of the daily load curve for residential electricity usage in the smart grid'

机译:再次参与“智能电网中居民用电的每日负荷曲线的短期预测”的讨论

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We thank the discussants for their comments and for their insights into the problems of electricity load modeling and forecasting. The discussants provided many proposals for modifying or extending our load model that are worthy of investigation. We emphasize that our work is ultimately intended for use in industrial applications and reflects the requirements of these applications: producing accurate forecasts quickly, with robustness to irregularities in the data and adaptability to changing conditions with minimal need for calibration data. These requirements influence both the structure of our model and our reaction to the discussants' proposals. For example, we do not expect to have the luxury of fitting the model to several years of historical data, as suggested by Espada and Durban, and even the use of the forecasting method of Martinez-Alvarez et al., as suggested by Aung, is not feasible for the OlyPen data because the method is based on the analysis of load curve patterns in historical data and effectively requires a full year of historical data for calibration.
机译:我们感谢讨论者的评论以及对电力负荷建模和预测问题的见解。讨论者提供了许多有关修改或扩展负载模型的建议,值得研究。我们强调,我们的工作最终旨在用于工业应用,并反映了这些应用的要求:快速生成准确的预测,具有对数据不规则性的鲁棒性和对变化条件的适应性,而对校准数据的需求最少。这些要求既影响我们模型的结构,也影响我们对讨论者建议的反应。例如,我们不希望像Espada和Durban所建议的那样将模型拟合到多年的历史数据,甚至不能像Aung所建议的那样使用Martinez-Alvarez等人的预测方法,对于OlyPen数据来说是不可行的,因为该方法基于对历史数据中负载曲线模式的分析,并且实际上需要一整年的历史数据才能进行校准。

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