DOI: 10.14704/nq.2018.16.6.1560

Application of Fuzzy Neural Network in Predicting Small Hydropower Short-term Generating Capacity

Zheng Mu, Zhiping Li, Hongguang Zhang


This paper proposes a prediction method on short-term generating capacity of small hydropower projects based on fuzzy neural network, taking into consideration of the implications of factors such as season, climate, rainfall, temperature, and dates influencing small hydropower generation in a short-time. The research subject is regional small hydropower project groups as a collective. It screens out sectional flow of large hydropower projects as a correlation factor. We then conduct analysis on the large and small hydropower projects and test for the significant difference. Based on the regression models for these two, we utilize the predicted value of large hydropower for prediction of the small hydropower’s capacity, thus providing an effective solution to resolve the difficulty in capacity prediction of small hydropower in short term. The application results prove that the method produces satisfactory prediction results and it provides valuable reference to the predictability of short-term small hydropower generating capacity.


Fuzzy Neural Network, Small-scale Hydrodynamic Power, Generation Capacity Prediction, Artificial Neuron, Biological Neuron Signal

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Abujazar MSS, Suja F, Ibrahim IA, Kabeel AE, Sharil S. Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model. Journal of Cleaner Production 2018; 170: 147-59.

Chen N, Gong HF, Wang MY. Clean use of coal under hazy conditions. Coal and Chemical Engineering 2017; 40(7): 158-60.

Delisle V, Kummert M. Cost-benefit analysis of integrating BIPV-T air systems into energy-efficient homes. Solar Energy 2016; 136: 385-400.

Dong Z, Qian ZD, Liu Y, Liu CD. Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network. Construction and Building Materials 2018; 158: 614-23.

He ZL, Lu MK, Wang ZZ, Gan XF. A wavelet neural network based on phase space reconstruction for radial power generation prediction. China Rural Water and Hydropower 2017; (9): 178-90.

Hunt, Kenneth J, Haas, Roland, Murray-Smith, Roderick. Extending the functional equivalence of radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks 1996; 7(3): 776-81.

Ioannis P, Athanasios P, Dagoumas S. Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model. Energy 2017; 118: 231-45.

Liu J, Yang JJ. Short-term output prediction of photovoltaic power generation based on improved GA-BP neural network. Journal of Shanghai University of Electric Power 2018; 34(1): 9-13.

Wang DH, Jia Q, Lin HY. Prediction of photovoltaic power generation based on modularized echo state neural network. Measurement & Control Technology 2018; 37(1): 59-63.

Wei W, Liang MH, Yin JH. Self-adaptive PID excitation control based on fuzzy RBF neural network. Corporate Technology and Development 2014; (7): 26-29.

Yan L, Niu DX, Wu HM. Daily maximum load forecasting based on Bayesian framework and echo state network. Grid Technology 2012; 36(11): 82-86.

Zhang X. Clean coal utilization development mode and technology requirements. Shanxi Chemical Industry 2017; 37(1): 121-23.

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