Open AccessApplication of artificial neural networks and fuzzy logic to long‐term load forecast considering the price elasticity of electricity demand
Author(s) -
Tondolo de Miranda Sandy,
Abaide Alzenira,
Sperandio Mauricio,
Santos Moises Machado,
Zanghi Eric
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2606
Subject(s) - price elasticity of demand , electricity , electricity market , demand response , artificial neural network , computer science , fuzzy logic , load profile , elasticity (physics) , smart grid , electric power system , demand forecasting , operations research , econometrics , economics , microeconomics , engineering , artificial intelligence , power (physics) , materials science , physics , quantum mechanics , electrical engineering , composite material
Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.