Thermal generating unit commitment using an extended mean field annealing neural network Export

@article{citeulike:715554, abstract = {An extended mean field annealing neural network approach is used for short-term thermal unit commitment. In power systems, the major goal of the generating unit commitment is to minimise the total fuel cost of the thermal units subject to some practical constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. The annealing neural network combines good solution quality for simulated annealing with rapid convergence for artificial neural network. The extended mean field annealing neural network is used to find short-term thermal unit commitment. By doing so, it can help in finding the optimum solution rapidly and efficiently. The effectiveness of the proposed approach is demonstrated by thermal unit commitment of the Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper unit commitment}, author = {Liang, R. H. and Kang, F. C.}, citeulike-article-id = {715554}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=848586}, comment = {Good describtion of the UC-Model used (Taiwan Power System)}, journal = {Generation, Transmission and Distribution, IEE Proceedings-}, keywords = {artificial-neural-networks, could-be-helpful, energy, thermal-unit-commitment, unit-commitment}, number = {3}, pages = {164--170}, posted-at = {2006-06-29 12:21:29}, priority = {2}, title = {Thermal generating unit commitment using an extended mean field annealing neural network}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=848586}, volume = {147}, year = {2000} }

TY - JOUR ID - citeulike:715554 L3 - citeulike-article-id:715554 N2 - An extended mean field annealing neural network approach is used for short-term thermal unit commitment. In power systems, the major goal of the generating unit commitment is to minimise the total fuel cost of the thermal units subject to some practical constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. The annealing neural network combines good solution quality for simulated annealing with rapid convergence for artificial neural network. The extended mean field annealing neural network is used to find short-term thermal unit commitment. By doing so, it can help in finding the optimum solution rapidly and efficiently. The effectiveness of the proposed approach is demonstrated by thermal unit commitment of the Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper unit commitment IS - 3 JF - Generation, Transmission and Distribution, IEE Proceedings- EP - 170 TI - Thermal generating unit commitment using an extended mean field annealing neural network VL - 147 SP - 164 KW - artificial-neural-networks KW - could-be-helpful KW - energy KW - thermal-unit-commitment KW - unit-commitment N1 - Good describtion of the UC-Model used (Taiwan Power System) AU - Liang, RH AU - Kang, FC PY - 2000/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=848586 ER -