Application of artificial intelligence and non-intrusive energy-managing system to economic dispatch strategy for cogeneration system and utility

This paper proposes that artificial intelligence techniques and non-intrusive energy-managing technology (NIEM) will effectively manage energy demands within economic dispatch strategy analysis for the cogeneration plant and power utility. To test the performance of the proposed approach, data sets for electrical loads in the factories were analyzed and established using an electromagnetic transient program (EMTP) and onsite load measurement. The artificial intelligence techniques were applied to data extraction and factory load identification, especially for non-intrusive energy management. The effectiveness of factory load identification was analyzed and compared using different classifier methods. The strategy analysis revealed that the analysis of economic dispatch strategy for the cogeneration plant and power utility in the way of energy demands using the NIEM can estimate reasonably energy contribution from the cogeneration plant and/or power utility, and further improve air pollution. The application of artificial intelligence can reduce greatly the computation time and the size of memory for factory load identification and energy calculation.