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

TL;DR

This paper introduces an LSTM-based model that incorporates textual news features to enhance electricity demand forecasting accuracy and reduce uncertainty, outperforming traditional benchmarks.

Contribution

It is the first to integrate textual news data into LSTM models for electricity demand prediction, improving accuracy and confidence interval narrowing.

Findings

Textual news features improve forecast accuracy by over 3%.

Model reduces forecasting uncertainty and narrows confidence intervals.

Sentiment and geopolitics-related words influence demand predictions.

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.