The present study is focused on assessing the impact of the performance of baseline load prediction pipelines on the estimation (by the grid operator) accuracy of the flexibility offered by different categories of buildings. Accordingly, the corresponding impact of employing different machine learning (ML) algorithms, with sliding-window and offline training schemes, for hour-ahead baseline load prediction has been investigated and compared. Using a smart meter measurements dataset, training window sizes and the most promising pipeline for each building category are first identified. Next, the consumption profiles of five buildings (belonging to each category), with the regular operation (baseline load) and while offering flexibility, are physically simulated. Finally, the identified pipelines are used for predicting the baseline loads, and the resulting error in estimating the provided flexibility is determined. Obtained results demonstrate that the identified most promising prediction pipeline (extra trees algorithm with a sliding window of 5 weeks) offers a notably superior performance compared to that of offline training (average $R^2$ score of 0.91 vs. 0.87). Employing these pipelines permits estimating the provided flexibility with acceptable accuracy (flexibility index's mean relative error between -2.45% to +2.79%), permitting the grid operator to guarantee fair compensation for buildings' offered flexibility.

本研究的重点是评估基线负荷预测管道的性能对不同类别建筑物提供的灵活性的估计（由电网运营商）准确性的影响。因此，研究和比较了采用不同机器学习 (ML) 算法、滑动窗口和离线训练方案对提前一小时基线负荷预测的相应影响。使用智能电表测量数据集，首先确定每个建筑类别的训练窗口大小和最有希望的管道。接下来，对五个建筑物（属于每个类别）的消耗曲线进行物理模拟，这些建筑物具有常规操作（基准负载）并提供灵活性。最后，识别出的管道用于预测基线负载，并确定在估计提供的灵活性时产生的错误。获得的结果表明，与离线训练（平均$R^{\mathrm{2个}}$得分为 0.91 与 0.87）。使用这些管道可以以可接受的精度估算提供的灵活性（灵活性指数的平均相对误差在 -2.45% 到 +2.79% 之间），从而允许电网运营商保证对建筑物提供的灵活性进行公平补偿。