Short-Term Load Forecasting Using Artificial Neural Network in Indonesia

Sylvia Jane Annatje Sumarauw(1*);

(1) Universitas Negeri Manado
(*) Corresponding Author



Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating costs increase and ultimately lead to waste. STLF research in Indonesia, especially the State Electricity Company (PLN Sulselrabar), has yet to be widely used. Methods mainly used are manual and conventional methods because they are considered adequate. In addition, Indonesia's geographical conditions are extensive and diverse, and the electricity system is complex. As a result, the factors affecting each country's electricity demand are different, so unique forecasting methods are needed. Artificial Neural Network (ANN) is one of the Artificial Intelligent (AI) methods widely used for STLF because it can model complex and non-linear relationships from networks. This paper aims to build an STLF forecasting model that is suitable for Indonesia's geographical conditions using several ANN models tested. Based on several ANN forecasting models, the test results obtained the best model is Model-6 with ANN architecture (9-20-1). This model has one hidden layer, 20 neurons in the hidden layer, a sigmoid logistic activation function (binary sigmoid), and a linear function. Forecasting performance values obtained mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 430.48 MW2, 15.07 MW, and 2.81%, respectively.


Short-Term Load Forecast; Artificial Neural Network; Forecasting Performance; State Electricity Company


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S. Fan and L. Chen, “Short-term load forecasting based on an adaptive hybrid method,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 392–401, 2006.

S. K. Sheikh and M. G. Unde, “S -t l f ann t,” International Journal of Engineering Sciences & Emerging Technologies, vol. 1, no. 2, pp. 97–107, 2012.

H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: Literature survey and classification of methods,” Int J Syst Sci, vol. 33, no. 1, pp. 23–34, 2002, doi: 10.1080/00207720110067421.

J. Sun, H. Dong, Y. Gao, Y. Fang, and Y. Kong, “The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai’an, Shandong Province, China,” Comput Intell Neurosci, vol. 2021, 2021, doi: 10.1155/2021/1502932.

Z. Shafiei Chafi and H. Afrakhte, “Short-Term Load Forecasting using Neural Network and Particle Swarm Optimization (PSO) Algorithm,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/5598267.

H. M. Al-Hamadi and S. A. Soliman, “Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model,” Electric Power Systems Research, vol. 68, no. 1, pp. 47–59, 2004, doi: 10.1016/S0378-7796(03)00150-0.

Y. Shen, Y. Ma, S. Deng, C.-J. Huang, and P.-H. Kuo, “An ensemble model based on deep learning and data preprocessing for short-term electrical load forecasting,” Sustainability, vol. 13, no. 4, p. 1694, 2021.

C. Nataraja, M. B. Gorawar, G. N. Shilpa, and S. Harsha.J, “Short Term Load Forecasting using Time Series Analysis: A Case Study for Karnataka, India,” International Journal of Engineering Science and Innovative Technology (IJESIT), vol. 1, no. 2, pp. 45–53, 2012.

M. Buhari and S. S. Adamu, “Short-term load forecasting using artificial neural network,” Lecture Notes in Engineering and Computer Science, vol. 2195, no. March, pp. 83–88, 2012, doi: 10.1109/icit.2000.854220.

A. K. Fard and M.-R. Akbari-Zadeh, “A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, no. 2, pp. 167–182, 2014.

J. Zhang, Y.-M. Wei, D. Li, Z. Tan, and J. Zhou, “Short term electricity load forecasting using a hybrid model,” Energy, vol. 158, pp. 774–781, 2018.

M. O. Okelola and S. Ayanlade, “An Artificial Neural Network Approach to Short-Term Load An Artificial Neural Network Approach to Short-Term Load Forecasting For Nigerian Electrical Power Network,” no. April 2022, 2021, doi: 10.17605/OSF.IO/539DV.

A. I. Arvanitidis, D. Bargiotas, A. Daskalopulu, V. M. Laitsos, and L. H. Tsoukalas, “Enhanced short-term load forecasting using artificial neural networks,” Energies (Basel), vol. 14, no. 22, Nov. 2021, doi: 10.3390/en14227788.

A. Yang, W. Li, and X. Yang, “Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines,” Knowl Based Syst, vol. 163, pp. 159–173, 2019.

H. Dong, Y. Gao, Y. Fang, M. Liu, and Y. Kong, “The short-term load forecasting for special days based on bagged regression trees in qingdao, China,” Comput Intell Neurosci, vol. 2021, 2021.

G. Dudek, “Short-term load forecasting using random forests,” in Intelligent Systems’ 2014, Springer, 2015, pp. 821–828.

M. N. JYOTHI, V. DINAKAR, and N. S. S. R. TEJA, “Narx Based Short Term Wind Power Forecasting Model,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 4, no. 4, p. 129, 2015, doi: 10.11591/ijai.v4.i4.pp129-138.

R. v Kanth and G. v Marutheswar, “Distribution System Short-TermLoad & Frequency Forecasting ( STLFF ) for OptimalUI C harges : ANeural-Wavelet based Approach,” International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 2, no. 7, pp. 142–147, 2013.

S. Annamareddi, S. Gopinathan, and B. Dora, “A simple hybrid model for short-term load forecasting,” Journal of Engineering (United States), vol. 2013, 2013, doi: 10.1155/2013/760860.

K. S. Rawat and G. H. Massiha, “Hardware Implementation of FIR Neural Network for Applications in Time Series Data Prediction,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 14, no. 1, pp. 130–139, 2015, doi: 10.11591/telkomnika.v14i1.7272.

A. Baliyan, K. Gaurav, and S. Kumar Mishra, “A review of short term load forecasting using artificial neural network models,” in Procedia Computer Science, 2015, vol. 48, no. C, pp. 121–125. doi: 10.1016/j.procs.2015.04.160.

S. A. Ilić, S. M. Vukmirović, A. M. Erdeljan, and F. J. Kulić, “Hybrid artificial neural network system for short-term load forecasting,” Thermal Science, vol. 16, no. SUPPL. 1, 2012, doi: 10.2298/TSCI120130073I.

L. Hernandez, C. Baladrón, J. M. Aguiar, B. Carro, A. J. Sanchez-Esguevillas, and J. Lloret, “Short-term load forecasting for microgrids based on artificial neural networks,” Energies (Basel), vol. 6, no. 3, pp. 1385–1408, 2013, doi: 10.3390/en6031385.

L. Hernández et al., “Artificial neural network for short-term load forecasting in distribution systems,” Energies (Basel), vol. 7, no. 3, pp. 1576–1598, 2014, doi: 10.3390/en7031576.

S. Rodygina, V. Lyubchenko, and A. Rodygin, “Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting,” Applied Mechanics and Materials, vol. 792, pp. 312–316, 2015, doi: 10.4028/

C. Ge, L. Wang, and H. Wang, “Power system short-term load forecasting based on fuzzy neural network,” Research Journal of Applied Sciences, Engineering and Technology, vol. 6, no. 16, pp. 2972–2975, 2013, doi: 10.19026/rjaset.6.3680.

Y. Y. Hsu, T. T. Tung, H. C. Yeh, and C. N. Lu, “Two-Stage Artificial Neural Network Model for Short-Term Load Forecasting,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 678–683, 2018, doi: 10.1016/j.ifacol.2018.11.783.

I. Samuel, T. Ojewola, A. Awelewa, and P. Amaize, “Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods,” IOSR Journal of Electrical and Electronics Engineering Ver. III, vol. 11, no. 1, pp. 2278–1676, 2016, doi: 10.9790/1676-11137281.

S. Singh, S. Hussain, and M. A. Bazaz, “Short term load forecasting using artificial neural network,” 2017 4th International Conference on Image Information Processing, ICIIP 2017, vol. 2018-Janua, no. December, pp. 159–163, 2018, doi: 10.1109/ICIIP.2017.8313703.

H. Patel, “Application of Artificial Neural Network for Short Term Load Forecasting,” International Journal of Advance Engineering and Research Development, vol. 2, no. 04, 2015, doi: 10.21090/ijaerd.020439.

H. Dong, Y. Gao, X. Meng, and Y. Fang, “A multifactorial short-term load forecasting model combined with periodic and non-periodic features-a case study of qingdao, China,” IEEE Access, vol. 8, pp. 67416–67425, 2020.

G. Veljanovski, M. Atanasovski, M. Kostov, and P. Popovski, Application of Neural Networks for Short Term Load Forecasting in Power System of North Macedonia. 2020. [Online]. Available:

P. Ray, D. P. Mishra, and R. K. Lenka, “Short term load forecasting by artificial neural network,” 2016 International Conference on Next Generation Intelligent Systems, ICNGIS 2016, no. September, 2017, doi: 10.1109/ICNGIS.2016.7854003.

S. A. W. Dinata, M. Azka, P. Hasanah, S. Suhartono, and M. D. H. Gamal, “Comparison of Short-Term Load Forecasting Based on Kalimantan Data,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 2, pp. 243–259, 2021.

T. E. Putri, A. A. Firdaus, and W. I. Sabilla, “Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network,” Journal of Information Systems Engineering and Business Intelligence, vol. 4, no. 2, pp. 96–105, 2018.

E. N. Yaqin et al., “Short Term Load Forecasting for Weekends in Indonesia: Comparison of Three Methods,” in IOP Conference Series: Materials Science and Engineering, 2018, vol. 288, no. 1, p. 012119.

S. J. A. Sumarauw, E. Winarko, and R. Wardoyo, “Weighting Customers’data for More Accurate Short-Term Load Forecast,” J Theor Appl Inf Technol, vol. 90, no. 2, p. 23, 2016.

T. Tumiran, S. Sarjiya, L. M. Putranto, E. N. Putra, R. F. S. Budi, and C. F. Nugraha, “Long-Term Electricity Demand Forecast Using Multivariate Regression and End-Use Method: A Study Case of Maluku-Papua Electricity System,” in 2021 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), 2021, pp. 258–263.


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