Ann-Trained Using Bat Algorithm for Modeling University-Based Energy Consumption on Short Term Basis

Okelola, Muniru Olajide, Amole Abraham Olatide


Adequate planning and right decision making in the energy sector lies on accurate forecasts of the load demand. In this paper, Artificial Neural Network (ANN) trained via Bat algorithm was employed for short term load projection of University of Ibadan, Nigeria. Daily load demand of the study area was obtained from the log records. The neural network was built, trained with historical data gotten from the premier University in Nigeria and then used to predict 24 hour’s load demand from Dec., 1st to Dec., 7th, 2016. The experimental results indicated that the proposed method achieved a Mean Absolute Percentage Error (MAPE) of 6.60% and a Mean Percentage Error (MPE) of 4.17%. This research finds application in scheduling of power demand in power system.

Keywords: Artificial Neural Network, Bat algorithm, Mean Absolute Percentage Error, Mean Percentage Error, Short-term forecasting,

DOI: 10.7176/JIEA/10-2-04

Publication date:March 31st 2020

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ISSN (Paper)2224-5782 ISSN (Online)2225-0506
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