We propose an innovative electricity demand forecasting framework based on three model-selection techniques to maximize the forecasting accuracy. In the framework, we forecast the day-ahead electricity demand every 15 minutes based on temperature and cloud data, which are sampled from 35 weather stations. We develop three progressive techniques for selecting the model and data. First, using a stagewise forward technique, we select highly-correlated weather stations and group the best combination of selected stations. Second, using a piecewise series technique, we select the best performing forecasting machine every hour by comparing the forecasting accuracy of four forecasting machines. Third, we develop a pairwise mapping technique to combine two tandem forecasting models at the smaller sampling interval when the sampling intervals of weather and demand data differ. We verify that the framework based on three selection techniques results in higher forecasting accuracy using data from the 2018 RTE demand forecasting competition held in France.

中文翻译：

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具有分阶段、分段和成对选择技术的需求预测框架


我们提出了一种基于三种模型选择技术的创新电力需求预测框架，以最大限度地提高预测准确性。在该框架中，我们根据从 35 个气象站采样的温度和云数据，每 15 分钟预测日前的电力需求。我们开发了三种渐进技术来选择模型和数据。首先，使用分阶段前向技术，我们选择高度相关的气象站并对所选气象站的最佳组合进行分组。其次，使用分段串联技术，我们通过比较四台预测机的预测精度，每小时选择性能最佳的预测机。第三，我们开发了一种成对映射技术，当天气和需求数据的采样间隔不同时，以较小的采样间隔组合两个串联预报模型。我们使用 2018 年法国 RTE 需求预测竞赛的数据验证了基于三种选择技术的框架具有更高的预测准确性。