Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

Iram Parvez, Jianjian Shen, Ishitaq Hassan and Nannan Zhang
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Iram Parvez: Institute of Hydropower and Hydro informatics, Dalian University of Technology, Dalian 116024, China
Jianjian Shen: Institute of Hydropower and Hydro informatics, Dalian University of Technology, Dalian 116024, China
Ishitaq Hassan: Department of Civil Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Nannan Zhang: Basin Projects Operation Management Department China three Gorges Limited Corporation, Yichang 443133, China

Energies, 2021, vol. 14, issue 2, 1-28

Abstract: The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production.

Keywords: short-term scheduling; cascaded hydropower plants; data mining techniques; energy production; generation schedules (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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