Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1) sample-based approaches, where each forecast is made independently, and (2) time series regression approaches, where some historical load and other feature information is used. When making a short-to-mid-term electricity demand forecast, some future information is available, such as the weather forecast and calendar variables. However, in existing forecasting models this future information is not fully incorporated. To overcome this limitation of existing approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting (MMMPF), a novel and general framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make probabilistic predictions. Experiments are performed on a real-world dataset for short-to-mid-term electricity demand forecasting for multiple regions and compared with various ML methods. They show that the proposed MMMPF framework outperforms not only sample-based methods but also existing time-series forecasting models with the exact same base models. Models trainded with MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future.

翻译：预测带有不确定性的电力需求有助于电网的规划与运行，从而为消费者提供可靠的电力供应。基于机器学习（ML）的需求预测方法可分为：（1）基于样本的方法，即每个预测独立进行；（2）时间序列回归方法，即利用部分历史负荷及其他特征信息。在进行中短期电力需求预测时，某些未来信息（如天气预报和日历变量）是可获取的。然而，现有预测模型并未充分利用这些未来信息。为克服这一局限，我们提出掩码多步多元概率预测（MMMPF），这是一种新颖且通用的框架，可训练任何能生成输出序列的神经网络模型，将过去的时间信息与已知的未来信息相结合以进行概率预测。实验基于真实数据集对多个区域的中短期电力需求进行预测，并与多种机器学习方法对比。结果表明，所提出的MMMPF框架不仅优于基于样本的方法，而且在采用相同基础模型的情况下，也超越了现有时间序列预测模型。基于MMMPF训练的模型还能生成所需分位数以捕捉不确定性，从而支持未来电网的概率规划。