Abstract
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.
Abstract (translated)
电力需求预测是一个已经建立的研究领域。通常,这项任务需要考虑历史负荷、天气预报、日历信息和已知的主要事件等因素。最近,人们开始关注从文本新闻中可能使用的新的信息来源,以改善这些预测的性能。本文提出了一个包含文本新闻特征的长短时记忆网络(LSTM),成功预测了英国的全国电力需求确定性和概率任务。研究发现,与交通和地缘政治相关的公众情绪和词向量表示对电力需求具有时间连续性的影响。实验结果显示,与纯LSTM基准相比,具有文本特征的LSTM提高了超过3%,与官方基准相比提高了接近10%。此外, proposed 模型通过缩小 confidence interval 和将预测分布更接近真相,有效地减少了预测不确定性。
URL
https://arxiv.org/abs/2309.06793
