Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers
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...
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doaj-d1aaebe433db49d69f9435ac5f1326292020-11-24T22:09:12ZengMDPI AGEnergies1996-10732017-10-011011172710.3390/en10111727en10111727Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type ConsumersAlexandru Pîrjan0Simona-Vasilica Oprea1George Căruțașu2Dana-Mihaela Petroșanu3Adela Bâra4Cristina Coculescu5Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, RomaniaDepartment of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, RomaniaDepartment of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, RomaniaDepartment of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, RomaniaDepartment of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, RomaniaDepartment of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, RomaniaThis 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.https://www.mdpi.com/1996-1073/10/11/1727energy consumptionforecasting solutionslarge non-household consumersartificial neural networksnon-linear autoregressive (NAR) modelnon-linear autoregressive with exogenous inputs (NARX) modelLevenberg-Marquardt (LM)Bayesian Regularization (BR)Scaled Conjugate Gradient (SCG) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexandru Pîrjan Simona-Vasilica Oprea George Căruțașu Dana-Mihaela Petroșanu Adela Bâra Cristina Coculescu |
spellingShingle |
Alexandru Pîrjan Simona-Vasilica Oprea George Căruțașu Dana-Mihaela Petroșanu Adela Bâra Cristina Coculescu Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers Energies 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) |
author_facet |
Alexandru Pîrjan Simona-Vasilica Oprea George Căruțașu Dana-Mihaela Petroșanu Adela Bâra Cristina Coculescu |
author_sort |
Alexandru Pîrjan |
title |
Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers |
title_short |
Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers |
title_full |
Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers |
title_fullStr |
Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers |
title_full_unstemmed |
Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers |
title_sort |
devising hourly forecasting solutions regarding electricity consumption in the case of commercial center type consumers |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2017-10-01 |
description |
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. |
topic |
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) |
url |
https://www.mdpi.com/1996-1073/10/11/1727 |
work_keys_str_mv |
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