Trading Electricity Markets Using Neural Networks

Laura Pozzetti, John P Cartlidge

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
555 Downloads (Pure)


We tackle the problem of developing an automated trading strategy to profit in the British intraday continuous electricity markets. We must train a feedforward neural network to predict one-hour-ahead total electricity transmission system demand. In live testing to ensure no look-ahead bias, we present results with accuracy better than National Grid’s own demand forecasts. We then train a second feedforward neural network, using our demand forecast as an input to the network, to predict one-hour-ahead net imbalance volume (NIV), and use this predicted NIV as a trading signal to buy and sell 30-minute electricity contracts. In live testing, between 09 March and 22 March 2020, the trading algorithm made 599 simulated trades, with 431 trades returning a profit (an accuracy of 72%). These results demonstrate the potential of neural network driven automated trading strategies to make significant risk-adjusted excess returns (i.e., profits) in the intraday electricity markets.
Original languageEnglish
Title of host publicationProceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020)
EditorsMichael Affenzeller, Agostino Bruzzone, Francesco Longo, Antonella Petrillo
Number of pages8
ISBN (Electronic)978-88-85741-44-7
Publication statusPublished - 16 Sept 2020
Event32nd European Modelling and Simulation Symposium - Virtual (Online), Athens, Greece
Duration: 16 Sept 202018 Sept 2020

Publication series

NameProceedings of the European Modeling & Simulation Symposium
PublisherCAL TEK
ISSN (Print)2724-0029


Conference32nd European Modelling and Simulation Symposium
Abbreviated titleEMSS
Internet address


  • Algorithmic trading
  • Energy trading
  • Forecasting Imbalance Volume
  • Forecasting electricity demand
  • Intraday trading
  • Electricity markets


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