概率电力需求预测与不确定性量化

中文摘要 AI 辅助

大多数关于电力消耗预测的研究都集中在确定性方法上，这些方法为预测范围内的每个时间步生成单一的点估计。然而，可再生能源渗透率的不断提高以及现代智能电网日益增长的复杂性，给电力系统的需求和运行带来了更大的变异性和不确定性。因此，概率预测（量化未来电力需求相关的不确定性和变异性）对于可靠的电力系统规划和运行变得越来越重要。本研究对四种当代概率预测模型进行了实证比较，以预测电力消耗，突出了它们各自的优势和局限性。我们在真实世界的电力系统相关数据集上进行了比较。在所有电力消耗区域中，NGBoost 表现出优越的概率预测性能，实现了最低的平均绝对误差（MAE）和均方根误差（RMSE），同时提供了校准良好的不确定性估计，具有高预测区间覆盖率且区间宽度合理。这些结果表明，对于所考虑的电力消耗数据，NGBoost 提供了比贝叶斯、蒙特卡洛（MC）Dropout 和高斯过程回归（GPR）模型更准确、更可靠的预测框架。

英文摘要

The majority of research on electricity consumption forecasting has focused on deterministic approaches, which generate a single point estimate for each time step in the forecasting horizon. However, the increasing penetration of renewable energy sources and the growing complexity of modern smart grids have introduced greater variability and uncertainty into power-system demand and operation. Consequently, probabilistic forecasting, which quantifies the uncertainty and variability associated with future electricity demand, is becoming increasingly important for reliable power-system planning and operation. This study presents an empirical comparison of four contemporary probabilistic forecasting models for electricity consumption, highlighting their respective strengths and limitations. We have performed comparision on real-world power systems related datasets. Across all power-consumption zones, NGBoost demonstrates superior probabilistic forecasting performance, achieving the lowest MAE and RMSE while providing well-calibrated uncertainty estimates with high prediction-interval coverage and reasonably narrow intervals. These results indicate that NGBoost offers a more accurate and reliable forecasting framework than Bayesian, Monte Carlo (MC) Dropout, and Gaussian Process Regression (GPR) models for the considered electricity consumption data.

发表机构

• Nepal College of Information Technology（尼泊尔信息技术学院）
• Nepal Telecommunications Authority（尼泊尔电信管理局）
• Nepal Electricity Authority（尼泊尔电力局）
• University of Bergen（卑尔根大学）
• Western Norway University of Applied Sciences（挪威西部应用科学大学）

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