Exogenous Data for Load Forecasting: A Review

Ramón Christen, Luca Mazzola, Alexander Denzler, Edy Portmann

2020

Abstract

Electrical power load forecasting defines strategies for utilities, power producers and individuals that participate in a smart grid. While it is well established in planning processes for production and utilities, the importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity market enables energy trading by individuals, requiring an accurate estimation of the production and consumption. Research on forecast for aggregated demand shows that including features for the forecast from sources, called exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their usage demonstrated to be able to explain the large variability observed in the power demand, taking into account the individual influences. Anyway, the influence of exogenous data is hardly investigated for individual forecasting, due to the minor prevalence of this analysis to date. This review shows the benefit of exogenous data usage and the necessity of detailed research on the input features and their influence on detailed, individual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open issues and research directions for electric smart communities that the authors would like to address.

Paper Citation

in Harvard Style

Christen R., Mazzola L., Denzler A. and Portmann E. (2020). Exogenous Data for Load Forecasting: A Review.In Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: CI4EMS, ISBN 978-989-758-475-6, pages 489-500. DOI: 10.5220/0010213204890500

in Bibtex Style

@conference{ci4ems20,
author={Ramón Christen and Luca Mazzola and Alexander Denzler and Edy Portmann},
title={Exogenous Data for Load Forecasting: A Review},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: CI4EMS,},
year={2020},
pages={489-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010213204890500},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: CI4EMS,
TI - Exogenous Data for Load Forecasting: A Review
SN - 978-989-758-475-6
AU - Christen R.
AU - Mazzola L.
AU - Denzler A.
AU - Portmann E.
PY - 2020
SP - 489
EP - 500
DO - 10.5220/0010213204890500