Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

Area of Science:

• Energy Systems Analysis
• Environmental Science
• Computational Intelligence

Background:

• Site selection for energy systems is complex, especially in imprecise environments.
• Environmental impact assessment is a critical, long-standing legislative requirement for power plant siting.
• Multi-criteria decision-making is essential for evaluating diverse power plant projects.

Purpose of the Study:

• To develop a decision-making model for ranking power plant projects.
• To evaluate projects based on multiple criteria including environmental, economic, and temporal factors.
• To provide a framework for selecting suitable power plant locations.

Main Methods:

• Utilized a multilayer perceptron (MLP) neural network.
• Employed a genetic algorithm (GA) for training the MLP model.
• Applied the model to rank power plant locations in India based on various attributes.

Main Results:

• Successfully ranked various power plant locations using the developed model.
• Demonstrated the effectiveness of GA-trained MLP for multi-criteria site selection.
• Provided a quantitative approach to power plant project evaluation.

Conclusions:

• The developed model offers a robust method for ranking power plant sites.
• Integrating environmental, economic, and project factors enhances decision-making accuracy.
• The methodology is applicable to energy infrastructure planning in diverse geographical contexts.