Artificial Neural Network (ANN) Backpropagation to Forecast 100% Renewable Energy in North Sumatra

Rimbawati; Himsar Ambarita; Tulus Burhanuddin Sitorus; M Irwanto

doi:10.63278/10.63278/mme.v31.1

Authors

Rimbawati Department of Mechanical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Indonesia
Himsar Ambarita Department of Mechanical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Indonesia
Tulus Burhanuddin Sitorus Department of Mechanical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Indonesia
M Irwanto University Malaysia Perlis (UniMAP), Malaysia

DOI:

https://doi.org/10.63278/10.63278/mme.v31.1

Keywords:

HRES, Algorithm, Optimization, FA, PSO.

Abstract

North Sumatra possesses abundant natural resources, including renewable energy sources such as water, geothermal, biomass, biogas, waste and solar energy. However, currently, these resources are not optimally utilized for electricity generation, with fossil fuel plants still dominating the energy mix. This research aims to establish an objective function model to optimize the use of renewable energy sources in North Sumatra by phasing out fossil fuel plants. The method employed in this study is a statistical approach based on secondary data from PT. PLN Persero in the North Sumatra region, which was optimized using the Firefly (FA) and Particle Swarm Optimization (PSO) algorithms. Analysis of the results reveals that the lowest absolute value of θ for |FA/RE–Thermal| in 2064 is 335.404192, while the lowest absolute value for |PSO/RE-Thermal| in 2065 is projected to reach 689.475978. A smaller absolute value of θ indicates closer proximity to the optimum value. Therefore, renewable energy optimization using the Firefly algorithm is predicted, using the Backpropagation Neural Network method, to be achievable in 2064, while with the PSO algorithm, it is expected to be realized in 2065, thus eliminating the need for thermal generators. The θ value serves as a parameter for measuring the discrepancy between predicted and actual values, with a smaller θ value indicating closer proximity to the optimum value. In the context of renewable energy optimization, the optimum value represents the level of renewable energy utilization required to supplant thermal generation. This research contributes by predicting that achieving 100% renewable energy utilization in North Sumatra can be attained by 2064 using the Firefly algorithm, based on the Backpropagation Neural Network method, and by 2065 using the PSO algorithm.

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