Application of genetic-based neural networks to thermal unit commitment Export

@article{citeulike:715650, abstract = {A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach}, author = {Shyh-Jier-Huang and Huang, Ching-Lien}, citeulike-article-id = {715650}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=589634}, journal = {Power Systems, IEEE Transactions on}, keywords = {artificial-neural-networks, dynamic-programming, energy, genetic-algorithms, hybrid-models, not-so-helpful, thermal-unit-commitment, unit-commitment}, number = {2}, pages = {654--660}, posted-at = {2006-06-29 13:24:56}, priority = {2}, title = {Application of genetic-based neural networks to thermal unit commitment}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=589634}, volume = {12}, year = {1997} }

TY - JOUR ID - citeulike:715650 L3 - citeulike-article-id:715650 N2 - A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach IS - 2 JF - Power Systems, IEEE Transactions on EP - 660 TI - Application of genetic-based neural networks to thermal unit commitment VL - 12 SP - 654 KW - artificial-neural-networks KW - dynamic-programming KW - energy KW - genetic-algorithms KW - hybrid-models KW - not-so-helpful KW - thermal-unit-commitment KW - unit-commitment AU - Shyh-Jier-Huang AU - Huang, Ching-Lien PY - 1997/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=589634 ER -