Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncertainties and fluctuations are averaged out; hence predicting load is more straightforward. This paper proposes a method called Forecasting using Matrix Factorization (\textsc{fmf}) for short-term load forecasting (\textsc{stlf}). \textsc{fmf} only utilizes historical data from consumers' smart meters to forecast future loads (does not use any non-calendar attributes, consumers' demographics or activity patterns information, etc.) and can be applied to any locality. A prominent feature of \textsc{fmf} is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers). We empirically evaluate \textsc{fmf} on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. The computational complexity of \textsc{fmf} is also substantially less than known methods for \textsc{stlf} such as long short-term memory neural networks, random forest, support vector machines, and regression trees.
翻译:准确的短期负载预报对于电力部门的高效运行至关重要。 预测单个家庭小时负荷等细颗粒值的负载由于波动性和内在的随机性较高而具有挑战性。 在总水平上, 如电网的月负荷, 不确定性和波动是平均的; 因此预测负载比较简单。 本文提出了一个方法, 叫做使用矩阵系数( textsc{ fmf}) 预测短期负载预报(\ textsc{ stlf}) 。\ textsc{ fmf} 只利用消费者智能网络的历史数据来预测未来负载( 不使用任何非日历属性、 消费者的人口或活动模式信息等) 。 在任何地点都可以应用。 很明显的 \ textsc{ fmf} 特征是, 它在时间( 从一个小时到天) 和空间层面( 消费者群体) 。 我们通过实验性地评估了消费者智能网络的智能仪表数据支持度 { { flormax floral- sell rolex) 。 在三个已知的计算方法上, 也明显地标定了 。