Decision tree-based approach for online management of PEM fuel cells for residential application
This thesis demonstrates a new intelligent technique for the online optimal management of PEM fuel cells units for onsite energy production to supply residential utilizations. Classical optimization techniques are based on offline calculations and cannot provide the necessary computational speed...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/2184/ http://umpir.ump.edu.my/id/eprint/2184/1/MOHD_RUSLLIM_BIN_MOHAMED.PDF |
| Summary: | This thesis demonstrates a new intelligent technique for the online optimal
management of PEM fuel cells units for onsite energy production to supply residential
utilizations. Classical optimization techniques are based on offline calculations and
cannot provide the necessary computational speed for online performance. In this
research, a Decision Tree (DT) algorithm is employed to obtain the optimal, or quasioptimal,
settings of the fuel cell online and in a general framework. The main idea is to
employ a classification technique, trained on a sufficient subset of data, to produce an
estimate of the optimal setting without repeating the optimization process. A database is
extracted from a previously-performed Genetic Algorithm (GA)-based optimization has
been used to create a suitable decision tree, which was intended for generalizing the
optimization results. The approach provides the flexibility of adjusting the settings of
the fuel cell online according to the observed variations in the tariffs and load demands.
Results at different operating conditions are presented to confirm the high accuracy of
the proposed generalization technique. The accuracy of the decision tree has been tested
by evaluating the relative error with respect to the optimized values. Then, the
possibility of pruning the tree has been investigated in order to simplify its structure
without affecting the accuracy of the results. In addition, the accuracy of the DTs to
approximate the optimal performance of the fuel cell is compared to that of the Artificial
Neural Networks (ANNs) used for the same purpose. The results show that the DTs can
somewhat outperform the ANNs with certain pruning levels. |
|---|