Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks

Karl Mason, Jim Duggan and Enda Howley

Energy, 2018, vol. 155, issue C, 705-720

Abstract: The ability to accurately predict future power demands, power available from renewable resources and the environmental impact of power generation is vital to the energy sector for the purposes of planning, scheduling and policy making. Machine learning techniques, neural networks in particular, have proven to be very effective methods for addressing these challenging forecasting problems. This research utilizes the powerful evolutionary optimisation algorithm, covariance matrix adaptation evolutionary strategy, as a means of training neural networks to predict short term power demand, wind power generation and carbon dioxide intensity levels in Ireland over a two month period. The network is trained over one month and then tested over the following month. A neural network trained with covariance matrix adaptation evolutionary strategy performs very competitively when compared to other state of the art prediction methods when forecasting Ireland's energy needs, providing fast convergence, more accurate predictions and robust performance. The covariance matrix adaptation evolutionary strategy trained network also gives accurate predictions when predicting multiple time steps into the future.

Keywords: Wind power generation; Power demand; CO2; Forecasting; Neural networks; Covariance matrix adaptation evolutionary strategy (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421830817X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:155:y:2018:i:c:p:705-720

DOI: 10.1016/j.energy.2018.04.192

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().