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:预测具有不确定性的电力需求有助于电网的规划和运行,以便向消费者提供可靠的电力供应。基于机器学习(ML)的需求预测方法可以分为:(1)基于样本的方法,每种预测都是独立作出的,(2)时间序列回归方法,使用一些历史负荷和其他特征信息。在进行短期到中期电力需求预测时,可以提供一些未来信息,如天气预报和日历变量。然而,在现有预测模型中,这一未来信息没有完全纳入。为了克服现有方法的局限性,我们建议采用遮盖式多标准多变量预测(MMMMPF),这是一个新颖和一般的框架,用于培训能够产生一系列产出的任何神经网络模型,将过去的时间信息与已知的未来信息结合起来,以作出概率性预测。在为多个区域进行短期到中期电力需求预测和与多种ML方法比较时,我们建议采用MMPFF框架,这个框架不仅超越了模型的模型,而且能够生成一系列产出的神经网络数据序列模型。此外,还将利用现有的模型模型和模型基础模型和模型,从而能够预测。