Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network

Tesa Eranti Putri, Aji Akbar Firdaus, Wilda Imama Sabilla

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Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends’ revenue. This suggests that in Indonesia, weekends’ electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.


Revenue forecasting; Electricity consumption; Java-Bali Electricity System; Jordan Recurrent Neural Network

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N. Amjady, "Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network," IEEE Transactions On Power Systems, vol. 21, no. 2, pp. 887-896, May 2006.

Ö. F. Ertugrul, "Forecasting electricity load by a novel recurrent extreme learning," Electrical Power and Energy Systems, vol. 78, pp. 429 - 435, 2016.

G. Aneiros, J. Vilar and P. Raña, "Short-term forecast of daily curves of electricity demand and price," Electrical Power and Energy Systems, vol. 80, pp. 96 - 108, 2016.

W. Qun, Z. Yingbin, Z. Xinying, Q. Youming, W. Yize and Z. Zhisheng, "Short-term Load Forecasting Model Based on Ridgelet Neural Network Optimized by Particle Swarm Optimization Algorithm," in 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, 2017.

P. F. Pai and W. C. Hong, "Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms," Electric Power Systems Research, vol. 74, pp. 417 - 425, 2005.

H. Nurohmah, D. Ajiatmo, D. Lastomo and I. Robandi, "Peramalan Beban Jangka Pendek Hari Libur Nasional dengan Interval Type-2 Fuzzy Inference System pada Sistem Jawa-Bali," in Sentia, Malang, 2015.

M. Oveis Abedinia, N. Amjady and H. Zareipour, "A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems," IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 62-74, 2016.

P. Ayuningtyas, D. Triyanto and T. Rismawan, "Prediksi Beban Listrik pada PT.PLN (PERSERO) Menggunakan Regresi Interval dengan Neural Fuzzy," Jurnal Coding Untan, vol. 4, no. 1, pp. 1-10, 2016.

T. Vantuch, A. G. Vidal, A. P. Ramallo-González, A. F. Skarmeta and S. Misák, "Machine Learning based Electric Load Forecasting for Short and Long-term Period," in IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 2018.

H. Chen, C. A. Cañizares and A. Singh, "ANN-based Short-Term Load Forecasting in Electricity Markets," in IEEE Power Engineering Society Winter Meeting Conference Proceedings (Cat. No.01CH37194), Columbus, OH, USA, 2001.

M. Mordjaoui, S. Haddad, A. Medoued and A. Laouafi, "Electric Load Forecasting by Using Dynamic Neural Network," International Journal of Hydrogen Energy, vol. 42, pp. 17655-17663, 2017.

M. Rana and I. Koprinska, "Forecasting electricity load with advanced wavelet neural networks," Neurocomputing, vol. 182, pp. 118-132, 2016.

A. Dedinec, S. Filiposka, A. Dedinec and L. Kocarev, "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, vol. 115, pp. 1688-1700, 2016.

W. He, "Load Forecasting via Deep Neural Networks," Procedia Computer Science, vol. 122, pp. 308-314, 2017.

D. L. Marino, K. Amarasinghe and M. Manic, "Building Energy Load Forecasting using Deep Neural Networks," in 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016.

J. Zheng, C. Xu, Z. Zhang and X. Li, "Electric Load Forecasting in Smart Grid Using Long-Short-Term-Memory based Recurrent Neural Network," in Conference on Information Sciences and Systems (CISS), Baltimore, 2017.

Z. C. Lipton, "A Critical Review of Recurrent Neural Networks for Sequence Learning," May 2015. [Online]. Available: [Accessed 10 7 2018].

M. I. Jordan, "Serial Order: A parallel distributed processing," Advances in Psychology, vol. 121, pp. 471-495, 1997.

H. Hikawa and Y. Araga, "Study on Gesture Recognition System Using Posture Classifier and Jordan Recurrent Neural Network," in International Joint Conference on Neural Networks, California, 2011.

Z. Kasiran, Z. Ibrahim and M. S. M. Ribuan, "Mobile Phone Customers Churn Prediction using Elman And Jordan Recurrent Neural Network," in 7th International Conference on Computing and Convergence Technology (ICCCT), Seoul, 2012.


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