Electricity Demand Forecasting through Natural Language Processing with
  Long Short-Term Memory Networks
Yun Bai Simon Camal Andrea Michiorri
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.