Short term power load forecasting using Deep Neural Networks

Ud Din, Ghulam Mohi and Marnerides, Angelos (2017) Short term power load forecasting using Deep Neural Networks. In: 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE, USA. ISBN 9781509045891

[img]
Preview
PDF (IEEE_ICNC2017_crcpaper)
IEEE_ICNC2017_crcpaper.pdf - Accepted Version

Download (1MB)

Abstract

Accurate load forecasting greatly influences the planning processes undertaken in operation centres of energy providers that relate to the actual electricity generation, distribution, system maintenance as well as electricity pricing. This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead. The contribution behind this work lies with the utilisation of a time-frequency (TF) feature selection procedure from the actual “raw” dataset that aids the regression procedure initiated by the aforementioned DNNs. We show that the introduced scheme may adequately learn hidden patterns and accurately determine the short-term load consumption forecast by utilising a range of heterogeneous sources of input that relate not necessarily with the measurement of load itself but also with other parameters such as the effects of weather, time, holidays, lagged electricity load and its distribution over the period. Overall, our generated outcomes reveal that the synergistic use of TF feature analysis with DNNs enables to obtain higher accuracy by capturing dominant factors that affect electricity consumption patterns and can surely contribute significantly in next generation power systems and the recently introduced SmartGrid.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
85202
Deposited By:
Deposited On:
10 Mar 2017 13:12
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Nov 2020 08:13