## 4 Conclusions  
This study investigates the use of artificial intelligence to control the operation of the dam reservoir of HEPs to operate at maximum efficiency. No similar studies have been found; this study will be useful in resolving uncertainties regarding resources and production in a HEPP. Two ANN models were used in the study, and the contribution and results of the ANN models are evaluated. Additionally, the study was tested by applying it to different ANN models, and the best result was achieved with the MLP Levenberg-Marquardt (LM) algorithm, which is the ANN training function.  
The first phase of the study used an ANN to estimate the water coming into the dam reservoir of a HEPP, which predicted incoming water with 96.5% accuracy. Estimations on successive days showed that the error rate would increase for the first few hours following a sudden change of incoming water, and then the error rate would return to the expected value. It is expected that if the data set used in ANN training is enriched, the predictive ability would increase and the error rate improve, and long-term data collection would make prediction of incoming water more reliable and accurate.  
The second phase of the study estimated the MCP to determine the production capacity that provided the most efficient outcome in the energy market. The short-term data were evaluated hourly; the average hourly estimate was 90% of MCP. When evaluated on a daily basis, the estimate was 95% of MCP. When evaluating the benefits provided by the study, for example, on December 6, 2021, it was observed that the predictions made by the YSA model enabled the operation to achieve 37.21% higher production revenue. This study provided the Daruca-2 HEPP operation directorate with the opportunity to make energy production plans and energy sales with maximum profitability.  
It was concluded that when the estimate of water and MCP were made together as a hybrid study of a HEPP, production planning was made with an accuracy of 95%. The study has determined that utilizing the maximum amount of data possible for water forecasting and training the artificial neural network (ANN) for short-term market clearing price (MCP) prediction with the most up-to-date data leads to much more accurate results.  
Conceptualization S.I. and S.A.O.; methodology, S.I. and A.E.O.; software, S.I. and S.A.O.; validation, S.A.O.; formal analysis, S.A.O.; investigation, S.I.; resources, S.A.O.; data curation, S.I. and A.E.O.; writing--original draft preparation, S.A.O. and S.I.; writing--review and editing, S.I., A.E.O. and S.A.O.; visualization, S.A.O. and S.I.; supervision, S.A.O.; project administration, S.I. and S.A.O.; funding acquisition, S.A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors would like to thank [PERSON] for providing data for this article with his master's thesis titled \"Operation of hydroelectric power plants dam reservoirs with artificial intelligence\" (Master's thesis, Ordu University). The authors would like to thank [PERSON], [PERSON], and [PERSON] for providing data for this article with their study titled \"A Neural Network Model for Estimation of Maximum Next Day Energy Generation Capacity of a Hydropower Station: A Case Study from Turkey\".  
**Conflicts of Interest:** Author [PERSON] was employed by the company Daruca--2 Hydroelectric Power Plant. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.