Title :
Novel AI approaches in power systems
Author :
Viswanathan, V. ; Krishnan, V. ; Tsoukalas, L.H.
Author_Institution :
Sch. of Nucl. Eng., Purdue Univ., West Lafayette, IN, USA
Abstract :
The electric utility industry has been subjected to rapid deregulatory reforms which have lead to a significant increase in the complexity of the system from the viewpoint of management and control. In this context, traditional artificial intelligence (AI) approaches are no longer as accurate as we would like them to be. Hence, a new set of paradigms are required to take into account the increasing complexity. Firstly, more accurate load forecasting tools are required and we discuss recurrent neural networks (RNNs), hybrid neurofuzzy designs and modular neural networks as the emerging alternatives to the traditional feedforward networks. We then deal with the paradigm of neurofuzzy anticipatory systems, with application to power system control. We also discuss applications of “intelligent agents” to the areas of power system management and power trading in competitive markets
Keywords :
electricity supply industry; fuzzy neural nets; load forecasting; recurrent neural nets; software agents; artificial intelligence approaches; competitive markets; complexity; electric utility industry; feedforward network; hybrid neurofuzzy designs; intelligent agents; load forecasting tools; modular neural networks; neurofuzzy anticipatory systems; power system control; power system management; power trading; recurrent neural networks; Artificial intelligence; Control systems; Electrical equipment industry; Industrial control; Load forecasting; Neural networks; Power industry; Power system management; Power systems; Recurrent neural networks;
Conference_Titel :
Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on
Conference_Location :
Bethesda, MD
Print_ISBN :
0-7695-0446-9
DOI :
10.1109/ICIIS.1999.810277
