Abstract
Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.
Highlights
Hydropower is a renewable source of energy that is derived from the fast reservoir water flows through a turbine
Based on the availability of different measured parameters in the dam, one can choose which model is applicable for prediction of hydropower, and these different combinations strengthen the applicability of model in different data availability of the study
Some models were only based on inflow to the dam and rainfall such as: M1, M2, M3, M13, M14, M15, M18, M19 and M20. These models did not require the hydropower generation of the dam in previous time steps and, based on inflow and precipitation, can predict the hydropower generation in the plan
Summary
Hydropower is a renewable source of energy that is derived from the fast reservoir water flows through a turbine. One of the main purposes of dam construction is to generate the hydropower via installation of a hydropower plant near the dam site. The rate of hydropower generation depends on the dam height and the inflow to the dam reservoir. Hydropower is one of the Energies 2019, 12, 289; doi:10.3390/en12020289 www.mdpi.com/journal/energies. Energies 2019, 12, 289 major sources of power supply in each country. The power consumption varies strongly during the year. An insight on the value of hydropower energy to be produced in the coming months would be an important tool in managing the electricity distribution network and operation of the dam. Hydropower generation forecasting could be a key component in dam operation. Hamlet et al [1] evaluated a long-lead forecasting model in the Colombia river and stated that long-lead forecasting model led to an increase in annual revenue of approximately
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