Recurrent Neural Network Model for Forecasting Electricity Demand in Nigeria
| dc.contributor.author | Abdulsalam, K.A. | |
| dc.contributor.author | Adegbenro, O | |
| dc.contributor.author | Akinbulire, T.O. | |
| dc.date.accessioned | 2022-08-30T21:19:50Z | |
| dc.date.available | 2022-08-30T21:19:50Z | |
| dc.date.issued | 2016-12 | |
| dc.description.abstract | This work uses modular recurrent neural network to estimate the electricity demand in Nigeria from 2015 to 2050. The network is a 2-layer multi-input, single-output model with twelve neurons trained using Levenberg-Marquardt algorithm. The data structure used for training is cell array of sequential concurrent data. The Recurrent Neural Network model was simulated as Non-linear Auto Regressive with eXogenous (NARX) model in Matlab environment and the predicted load for 2015 is about 550GWh; and an expected demand increase of 7.5 % every five year. | en_US |
| dc.identifier.uri | https://ir.unilag.edu.ng/handle/123456789/11167 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Engineering Research | en_US |
| dc.subject | energy, Levenberg-Marquardt, modular network, neurons, power | en_US |
| dc.title | Recurrent Neural Network Model for Forecasting Electricity Demand in Nigeria | en_US |
| dc.type | Technical Report | en_US |