Power source scheduling and adaptive load management via a genetic algorithm embedded neural network

This paper describes in detail the development of a decision support information system which would assist industrial managers in scheduling their equipment investment, as well as, in formulating the operational strategies for the various power sources. Such a system would also help dealing with various load management scenarios such that the objective of both minimizing the demand charge as well as maximizing the operational capability of facilities could be achieved. The proposed object-oriented management information system provides a user-friendly user interface has been developed and tested with data obtained from a sample study performed on the Taiwan power system. The experimental evaluations have demonstrated the feasibility, adaptability and effectiveness of the proposed power source scheduling and load management strategies.

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