Open AccessDEVELOPMENT OF A SHORT-TERM PREDICTION SYSTEM FOR ELECTRICITY DEMAND
Author(s) -
Steven Van Vaerenbergh,
Alberto Salcines Menezo,
Oscar Cosido Cobos
Publication year - 2021
Publication title -
dyna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.177
H-Index - 11
eISSN - 1989-1490
pISSN - 0012-7361
DOI - 10.6036/9894
Subject(s) - electricity , computer science , term (time) , portfolio , electricity market , energy demand , electric power system , demand response , energy (signal processing) , demand forecasting , industrial engineering , operations research , environmental economics , power (physics) , engineering , economics , finance , statistics , physics , mathematics , quantum mechanics , electrical engineering
This article describes the development of a prediction method for the demand for electrical energy of a marketer's customer portfolio. The project is motivated by the economic benefit produced when the entity has accurate estimates of energy demand when buying energy in an electricity auction. The developed system is based on time series analysis and machine learning. As this system was part of a real-world project with data from a real environment, the article focuses on practical aspects of the design and development of system of these characteristics, such as the heterogeneity of data sources, and the delay in data availability. The predictions obtained by the developed system are compared with the results of a simple method used in practice.Keywords: energy demand prediction, electric power, machine learning, data-driven prediction.