Day-Ahead Price Forecasting in Great Britain’s BETTA Electricity Market

Research output: Contribution to conferencePaperpeer-review

437 Downloads (Pure)


The characteristics of commodities such as
electricity, natural gas and oil mean that standard statisticsbased
pricing and prediction models that are typically applied
in financial markets cannot readily be transferred and used as
energy pricing models. Therefore, we investigate the use of
computational intelligence-based approaches for electricity
price forecasting. This paper compares two models for dayahead
electricity price forecasting, an AdaBoosted ensemble
of the Extra-Trees algorithm and a Generalized Regression
Neural Network (GRNN). In this work both forecasting
models were applied to the national electricity market of Great
Britain, the British Energy and Electricity Trading
Arrangements (BETTA). The models were evaluated using the
mean absolute percentage error (MAPE) statistic and the
results show that the GRNN yielded a comparable forecasting
error to the AdaBoosted algorithm with a significantly faster
computation time.
Original languageEnglish
Publication statusAccepted/In press - 1 Sep 2018
EventSYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics - Benguluru, India
Duration: 18 Nov 201820 Nov 2018


Abbreviated titleCiFER


  • Extra-Trees
  • Generalized Regression Neural Networks
  • British Energy and Electricity Trading Arrangements (BETTA)
  • Machine Learning
  • Price Forecasting

Fingerprint Dive into the research topics of 'Day-Ahead Price Forecasting in Great Britain’s BETTA Electricity Market'. Together they form a unique fingerprint.

Cite this