Open AccessSpatial neural network for forecasting energy consumption of Palembang area
Author(s) -
Muhammad Rif’an,
D. Daryanto,
A. A. Gde Agung
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/3/033092
Subject(s) - artificial neural network , energy consumption , electricity , consumption (sociology) , backpropagation , computer science , population , product (mathematics) , artificial intelligence , engineering , mathematics , demography , geometry , social science , sociology , electrical engineering
Spatial Neural Network proposed as a new approach to determine the level of energy consumption in the future in the Palembang region, South Sumatra. Back-propagation is used to train Artificial Neural Networks. Population size, Gross Regional Domestic Product (GRDP), economic growth, Household Energy Consumption spatially per sub-district is used for this estimate. Data for 2008-2012 is used to train Spatial Neural Networks (ANN) and Data for 2013-2017 is used to validate this new approach to electricity demand prediction. The proposed Spatial ANN approach provides relatively good predictions about energy demand in acceptable errors and high accuracy for electricity demand predictions.