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.
Highlights
For the last 45 years, worldwide energy consumption has increased by more than 2.2-fold, with the non-residential sector accounting for the most significant percentage of the whole electricity consumption, ranging from 76% in 1971 to 78% in 2015 [1]
In the third stage of our methodology, comprising four steps, we have developed the artificial neural networks forecasting solution based on the non-linear autoregressive with exogenous inputs (NARX) model, using as exogenous variables, the meteorological and the time stamps datasets
4 steps, we have developed the artificial neural networks forecasting solution based on the NARX model, using as exogenous variables, the time stamps datasets
Summary
For the last 45 years, worldwide energy consumption has increased by more than 2.2-fold, with the non-residential sector accounting for the most significant percentage of the whole electricity consumption, ranging from 76% in 1971 to 78% in 2015 [1]. An important part of the advanced metering infrastructure systems consists of the smart metering devices, specialized electronic systems that are able to record, store and make available to the customers and suppliers more detailed information than was previously possible when using regular meters. In this context, the purpose of this paper is to develop an accurate forecasting solution that uses detailed consumption data retrieved from these devices, making it possible for large non-household electricity consumers to craft a personalized efficient consumption strategy that best suits their needs, achieving significant savings on the electricity costs
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