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

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Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
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Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania
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Department of Mathematics-Informatics, University Politehnica of Bucharest, Splaiul Independenței 313, Bucharest 060042, Romania
*
Author to whom correspondence should be addressed.
Energies 2017, 10(11), 1727; https://doi.org/10.3390/en10111727
Received: 24 September 2017 / Revised: 14 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
(This article belongs to the Section A: Electrical Engineering)
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. View Full-Text
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) 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)
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MDPI and ACS Style

Pîrjan, A.; Oprea, S.-V.; Căruțașu, G.; Petroșanu, D.-M.; Bâra, A.; Coculescu, C. Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers. Energies 2017, 10, 1727. https://doi.org/10.3390/en10111727

AMA Style

Pîrjan A, Oprea S-V, Căruțașu G, Petroșanu D-M, Bâra A, Coculescu C. Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers. Energies. 2017; 10(11):1727. https://doi.org/10.3390/en10111727

Chicago/Turabian Style

Pîrjan, Alexandru, Simona-Vasilica Oprea, George Căruțașu, Dana-Mihaela Petroșanu, Adela Bâra, and Cristina Coculescu. 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers" Energies 10, no. 11: 1727. https://doi.org/10.3390/en10111727

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