Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Alexandru Pîrjan, Simona-Vasilica Oprea, George Căruțașu, Dana-Mihaela Petroșanu, Adela Bâra and Cristina Coculescu
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Alexandru Pîrjan: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
Simona-Vasilica Oprea: Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania
George Căruțașu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
Dana-Mihaela Petroșanu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
Adela Bâra: Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania
Cristina Coculescu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
Authors registered in the RePEc Author Service: George Căruţaşu ()

Energies, 2017, vol. 10, issue 11, 1-36

Abstract: This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

Keywords: energy consumption; forecasting solutions; large non-household consumers; artificial neural networks; non-linear autoregressive (NAR) model; non-linear autoregressive with exogenous inputs (NARX) model; Levenberg-Marquardt (LM); Bayesian Regularization (BR); Scaled Conjugate Gradient (SCG) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:11:p:1727-:d:116733

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