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.

本文提出了一种利用文本新闻特征的长短期记忆网络（LSTM）来成功预测英国国家电力需求的确定性和概率任务。实验结果表明，带有文本特征的 LSTM 相对于纯 LSTM 基准模型改进了 3% 以上，相对于官方基准模型改进了近 10%。此外，该模型能够通过缩小置信区间和使预测分布更接近真实值来有效地降低预测的不确定性。

使用长短期记忆网络进行自然语言处理的电力需求预测

Electricity Demand Forecasting through Natural Language Processing with  Long Short-Term Memory Networks

As advanced machine learning systems' capabilities begin to play a
significant role in geopolitics and societal order, it may become imperative
that (1) governments be able to enforce rules on the development of advanced ML
systems within their borders, and (2) countries be able to verify each other's
compliance with potential future international agreements on advanced ML
development. This work analyzes one mechanism to achieve this, by monitoring
the computing hardware used for large-scale NN training. The framework's
primary goal is to provide governments high confidence that no actor uses large
quantities of specialized ML chips to execute a training run in violation of
agreed rules. At the same time, the system does not curtail the use of consumer
computing devices, and maintains the privacy and confidentiality of ML
practitioners' models, data, and hyperparameters. The system consists of
interventions at three stages: (1) using on-chip firmware to occasionally save
snapshots of the the neural network weights stored in device memory, in a form
that an inspector could later retrieve; (2) saving sufficient information about
each training run to prove to inspectors the details of the training run that
had resulted in the snapshotted weights; and (3) monitoring the chip supply
chain to ensure that no actor can avoid discovery by amassing a large quantity
of un-tracked chips. The proposed design decomposes the ML training rule
verification problem into a series of narrow technical challenges, including a
new variant of the Proof-of-Learning problem [Jia et al. '21].

该研究分析了通过监控用于大规模神经网络训练的计算硬件来实现高级机器学习系统开发规则的执法以及国家之间达成潜在未来国际协议的相互监督的机制