Non-linear autoregressive neural network (NARNET) with SSA filtering for a university energy consumption forecast

Paul A. Adedeji, Stephen Akinlabi, Oluseyi Ajayi, Nkosinathi Madushele

Research output: Contribution to journalConference articlepeer-review

30 Citations (Scopus)

Abstract

Energy consumption forecast is essential for strategic planning in achieving a sustainable energy system. The hemispherical seasonal dependency of energy consumption requires intelligent forecast. This paper uses a non-linear autoregressive neural network (NARNET) for energy consumption forecast in a South African University with four campuses, using three-year daily energy consumption data. Singular Spectrum Analysis (SSA) technique was used for the data filtering. Three window lengths (L=54, 103 and 155) were obtained using periodogram analysis and R-values of network training at these window lengths were compared. Filtered data at L=103 gave the best R-values of 0.951, 0.983, 0.945 and 0.940 for campus A, B, C, and D respectively. The network validation and a short-term forecast were performed. Forecast accuracies of 85.87%, 75.62%, 85.02% and 76.83% were obtained for campus A, B, C and D respectively. The study demonstrates the significance of data filtering in forecasting univariate autoregressive series.

Original languageEnglish
Pages (from-to)176-183
Number of pages8
JournalProcedia Manufacturing
Volume33
DOIs
Publication statusPublished - 2019
Event16th Global Conference on Sustainable Manufacturing, GCSM 2018 - Lexington, United States
Duration: 2 Oct 20184 Oct 2018

Keywords

  • Energy Forecast
  • Non-linear Autoregressive Neural Network
  • Singular Spectrum Analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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