Electricity demand forecasting is a well established research field. Usually this task is performed considering historical loads, weather forecasts, calendar information and known major events. Recently attention has been given on the possible use of new sources of information from textual news in order to improve the performance of these predictions. This paper proposes a Long and Short-Term Memory (LSTM) network incorporating textual news features that successfully predicts the deterministic and probabilistic tasks of the UK national electricity demand. The study finds that public sentiment and word vector representations related to transport and geopolitics have time-continuity effects on electricity demand. The experimental results show that the LSTM with textual features improves by more than 3% compared to the pure LSTM benchmark and by close to 10% over the official benchmark. Furthermore, the proposed model effectively reduces forecasting uncertainty by narrowing the confidence interval and bringing the forecast distribution closer to the truth.

翻译：电力需求预测是一个成熟的研究领域。通常，这一任务基于历史负荷数据、天气预报、日历信息以及已知重大事件进行。近年来，研究者开始关注如何利用文本新闻中的新兴信息源来提升预测性能。本文提出了一种融合文本新闻特征的长短期记忆（LSTM）网络，该网络能够成功预测英国国家电力需求的确定性及概率性任务。研究发现，与交通和地缘政治相关的公众情绪及词向量表征对电力需求存在时间连续性影响。实验结果表明，融合文本特征的LSTM相比纯LSTM基准模型性能提升超过3%，相较于官方基准模型提升近10%。此外，该模型通过缩窄置信区间、使预测分布更接近真实值，有效降低了预测不确定性。