Modeling of Energy Production of Sengguruh Hydropower Plant Using Neuro Fuzzy Network


  • Daniel Rohi Electrical Engineering Department, Petra Christian University, Surabaya, Indonesia
  • Hanny H. Tumbelaka Electrical Engineering Department, Petra Christian University, Surabaya, Indonesia


Artificial Intelligence, Hydropower Plant, Neuro-fuzzy, Renewable Energy,


The hydroelectric power plant needs to be operated carefully to obtain optimal results, as it is highly dependent on water availability. Factors to take into account are the water discharge and the duration of time for the operation. Decomposition analysis method is the method chosen to manage the operation of hydropower. This paper discusses the hydropower operation model using artificial intelligence with Neuro Fuzzy Takagi-Sugeno (NFTS) network technique. The Hydropower plants selected for modeling is Sengguruh Hydroelectric Power Plant with a capacity of 29 MW. This model was developed using three factors as inputs. They are the discharge of water, turbine water discharge and duration of operation time. The output is electric energy production. The data used is the operating data for one year, from January to December. The model testing shows satisfactory results as it reveals the real conditions and the errors occurred on the network was below 6.7%.


Adib, Rana, et al. "Renewables 2016 Global Status Report." Global Status Report Renewable Energy Policy Network for the 21st Century (REN21) (2016): 272.

Bartle, Alison. "Hydropower potential and development activities." Energy Policy 30.14 (2002): 1231-1239.

Rohi, Daniel, M. Bisri, and A. Lomi. "Dynamic System Models Sutami Hydropower Plant Indonesia to Calculate the Economic and Environmental Aspects of Hydropower Plant Operation." Applied Mechanics and Materials. Vol. 815. Trans Tech Publications, 2015.

ESDM Indonesia (2008), Handbook of Economi Energy Statistic in Indonesia 2006

Limantara, Lily Montarcih. "Optimization of Water Needs at Kepanjen Dam and Sengguruh Dam, East Java, Indonesia." International Journal of Academic Research 2.5 (2010).

Sukatja, Bambang. "The Problems of Small Reservoir That Built In River Basins with High Sedimentation Rate, a Case Study of Sengguruh Reservoir." International Journal of Academic Research 3.4 (2011).

Fidari, Jadfan Sidqi, Mohammad Bisri, and Ery Suhartanto. "Study of residual age estimation for sutami reservoir with sedimentation approach." Journal of Water Engineering 4.2 (2014).

Palit A. K., Popovic D. “Computational Intelligence in Time Series Forecasting, Theory and Engineering Applications”. Springer (2005)

Pasila, F., Alimin, R.” Applications of artificial intelligence control for Parallel Discrete-Manipulators”. 4th IGNITE Conference and 2016 International Conference on Advanced Informatics: Concepts, Theory and Application, ICAICTA (2016)

Alimin, R., Pasila, F.” Design of Two-Serial Hexapod of Discrete Manipulator”. Proceedings of International Conference on Computational Intelligence, Modelling and Simulation 2016- September,7579697, pp. 69-73 (2016)

Pasila, F., Alimin, R. “Coordinates modelling of the discrete hexapod manipulator via artificial intelligence”. Lecture Notes in Electrical Engineering 365, pp. 47-53 (2016)

Pasila, F., Ronni, S., Thiang, Wijaya, L.H. “Long-term forecasting in financial stock market using accelerated LMA on neuro-fuzzy structure and additional fuzzy C-means clustering for optimizing the GMFs”. Proceedings of the International Joint Conference on Neural Networks 4634367, pp. 3961-3966 (2008)

Pasila, F., Alimin, R., Natalius, H. “Neuro-fuzzy architecture of the 3D model of massive parallel actuators”. ARPN Journal of Engineering and Applied Sciences 9(12), pp. 2900-2905 (2014)

Whulanza, Y., Hadiputra, A.P., Pasila, F., Supriadi, S. “Electromechanical characterization of bucky gel actuator based on polymer composite PCL-PU-CNT for artificial muscle”. Lecture Notes in Electrical Engineering 365, pp. 185-192 (2016).




How to Cite

Rohi, D., & Tumbelaka, H. H. (2018). Modeling of Energy Production of Sengguruh Hydropower Plant Using Neuro Fuzzy Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 159–162. Retrieved from