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首页> 外文期刊>IEEE transactions on industrial informatics >An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting
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An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting

机译：基于在线校准时间序列的日前天然气需求预测模型

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摘要

This paper proposes a new online-calibrated time series based model with the application to the dayahead natural gas demand (GD) forecasting. A double-stage parallel process is developed for creating the forecasting model. The two stages include analysis of the temperature-independent and temperature-dependent components of the GD. The former stage is executed by online processing of the historical GD information considering the intertemporal variation of the GD. The latter stage, however, is conducted by exploiting the features of the GD information correlated with the ambient temperature. The forecast of the temperature is incorporated into the GD forecasting model through a correlation-based function. The model can generate the day-ahead GD forecast with both the hourly and intrahourly resolutions without compromising the forecast accuracy. The model is calibrated online using the historical GD and temperature information to achieve a higher forecast accuracy. The practical challenges associated with the industrial application of the model are also discussed. The application of the proposed model is numerically examined using real-world GD and temperature data, and the results are comprehensively studied. The outcomes reveal the efficacy and feasibility of the proposed model under various cases.
机译：本文提出了一种新的基于在线校准的时间序列的模型，并将其应用于日前天然气需求（GD）预测中。开发了一个两阶段并行过程来创建预测模型。这两个阶段包括对GD的温度无关和温度相关成分的分析。前一阶段是通过考虑GD的跨时变化对历史GD信息进行在线处理来执行的。但是，后一阶段是通过利用与环境温度相关的GD信息的特征来进行的。通过基于相关的函数将温度的预测合并到GD预测模型中。该模型可以生成小时和小时内分辨率的日前GD预测，而不会影响预测准确性。该模型使用历史GD和温度信息在线校准，以实现更高的预测精度。还讨论了与模型的工业应用相关的实际挑战。利用真实的GD和温度数据对所提出模型的应用进行了数值检验，并对结果进行了全面研究。结果揭示了所提出模型在各种情况下的有效性和可行性。

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