Widi Aribowo
The improvement of the economy country along with developments in each sector will be followed by technological progress. This has led to an increase in electricity demand in Indonesia every year. Electricity load forecasting has an important role in the energy management system. The purpose of electricity load forecasting is as an effort to balance between electricity demand and electricity supply. Power system management can be said to be good if the planning carried out will make a major contribution to the development of electric power systems. In this study, the Feed Forward Backpropagation Neural Network method (FFBNN) is optimized using the Teaching-Learning-Based Optimization Algorithm (TLBO) to predict long-term electricity load. The results were measured and validated using the Mean Absolute Percentage Error (MAPE) method. The performance of the proposed method will be compared with the actual data, feed forward backpropagation neural network (FFBNN) and cascade forward backpropagation neural network (CFBNN). It was found that the TLBO-FFBNN method had an average MAPE value of 0.00004936%. The Comparison results show the effectiveness of the proposed method. The proposed method shows that is adapted and has a good MAPE value. © 2022, International Journal of Intelligent Engineering and Systems. All Rights Reserved.
Departement of Electrical Engineering, Universitas Negeri Surabaya, Indonesia
