Name
Title | ||
|---|---|---|
Power plant start-up scheduling: a reinforcement learning approach combined with evolutionary computation |
Abstract | ||
|---|---|---|
Power plant start-up scheduling is aimed at minimizing the start-up time while limiting turbine rotor stresses to acceptable values. In order to increase on-line performance of searching an optimal or near-optimal start-up schedule during power plant operation, we propose to integrate neural network-based reinforcement learning with evolutionary computation implemented by means of Genetic Algorithms (GA). GA guides reinforcement learning to learn optimal schedules with respect to a number of representative sets of stress limits prior to the start-up process. During start-up, GA combined with reinforcement learning will search an optimal or near-optimal start-up schedule at a given set of stress limits. This approach significantly reduces the time needed for learning and searching. On a SPARC station 20, experiments show that it can search an optimal or near-optimal schedule within tens of seconds of CPU time, a time range which should be acceptable in power plant operations. |
Year | Venue | Keywords |
|---|---|---|
1998 | Journal of Intelligent and Fuzzy Systems | power plant start-up scheduling,optimal schedule,GA guides reinforcement,evolutionary computation,start-up process,CPU time,time range,near-optimal start-up schedule,reinforcement learning,power plant operation,start-up time |
DocType | Volume | Issue |
Journal | 6 | 1 |
ISSN | Citations | PageRank |
1064-1246 | 4 | 0.77 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akimoto Kamiya | 1 | 33 | 5.28 |
| Hajime Kimura | 2 | 4 | 0.77 |
| Masayuki Yamamura | 3 | 242 | 37.62 |
| Shigenobu Kobayashi | 4 | 791 | 98.15 |