Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
Artificial Neural Networks (ANNs) are general purpose optimization techniques based on principles inspired from biological neurons in the brain which consists of a number of simple and highly interconnected processors (neurons). ANN has proved to be able to solve optimization problems in power sy...
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| Format: | Student Project |
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Faculty of Information Technology and Quantitative Sciences
2003
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| Online Access: | https://ir.uitm.edu.my/id/eprint/1985/ |
| _version_ | 1848802616284282880 |
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| author | Raja Zolkiply, Raja Ezham Shariffudin |
| author_facet | Raja Zolkiply, Raja Ezham Shariffudin |
| author_sort | Raja Zolkiply, Raja Ezham Shariffudin |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | Artificial Neural Networks (ANNs) are general purpose optimization techniques based
on principles inspired from biological neurons in the brain which consists of a number
of simple and highly interconnected processors (neurons). ANN has proved to be able
to solve optimization problems in power system. One of the areas in power system
operation that requires optimal solution is the Unit Commitment (UC) problem. The
UC problem involves determination of start-up and shut-down schedule of generating
units, and indirectly determines the optimum power should be generated by each unit
committed over a period of time to meet the forecasted load demand at minimum cost.
Besides that, the commitment schedule must satisfy other constraints in order to
minimize the total production cost. Therefore, this constitutes a problem to the
operators, where they find it difficult to make the decision manually on which unit to
keep online, and which unit to switch to offline, in order to minimize the production
cost. The ANN approach has proved to be able to solve the UC problem but involve
several problems such as divergence, excessive computation time, too much iteration
for solving small task and so on. In order to improve the implementation of ANN for
solving UC problem in power systems, this paper presents a comparison study
between standard backpropagation algorithm, extended backpropagation algorithms
and hybrid approach. The ANN used to forecast the power of four generating units in
a small power system consists of multilayer neural network which consists of three
input nodes, several hidden nodes and five output nodes. Besides that, input
parameters involve are current stage of load demand (first stage to sixth stage), current
load demand (megawatt) and previous load demand (megawatt), while the outputs of
the neural netwoiic are power generated by four thermal units at current stage and total
cost. |
| first_indexed | 2025-11-14T21:26:11Z |
| format | Student Project |
| id | uitm-1985 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:26:11Z |
| publishDate | 2003 |
| publisher | Faculty of Information Technology and Quantitative Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-19852017-04-19T09:13:46Z https://ir.uitm.edu.my/id/eprint/1985/ Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply Raja Zolkiply, Raja Ezham Shariffudin Artificial Neural Networks (ANNs) are general purpose optimization techniques based on principles inspired from biological neurons in the brain which consists of a number of simple and highly interconnected processors (neurons). ANN has proved to be able to solve optimization problems in power system. One of the areas in power system operation that requires optimal solution is the Unit Commitment (UC) problem. The UC problem involves determination of start-up and shut-down schedule of generating units, and indirectly determines the optimum power should be generated by each unit committed over a period of time to meet the forecasted load demand at minimum cost. Besides that, the commitment schedule must satisfy other constraints in order to minimize the total production cost. Therefore, this constitutes a problem to the operators, where they find it difficult to make the decision manually on which unit to keep online, and which unit to switch to offline, in order to minimize the production cost. The ANN approach has proved to be able to solve the UC problem but involve several problems such as divergence, excessive computation time, too much iteration for solving small task and so on. In order to improve the implementation of ANN for solving UC problem in power systems, this paper presents a comparison study between standard backpropagation algorithm, extended backpropagation algorithms and hybrid approach. The ANN used to forecast the power of four generating units in a small power system consists of multilayer neural network which consists of three input nodes, several hidden nodes and five output nodes. Besides that, input parameters involve are current stage of load demand (first stage to sixth stage), current load demand (megawatt) and previous load demand (megawatt), while the outputs of the neural netwoiic are power generated by four thermal units at current stage and total cost. Faculty of Information Technology and Quantitative Sciences 2003 Student Project NonPeerReviewed Raja Zolkiply, Raja Ezham Shariffudin (2003) Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply. (2003) [Student Project] (Submitted) |
| spellingShingle | Raja Zolkiply, Raja Ezham Shariffudin Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title | Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title_full | Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title_fullStr | Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title_full_unstemmed | Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title_short | Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply |
| title_sort | application of artificial neural network for solving unit commitment problem / raja ezham shariffudin raja zolkiply |
| url | https://ir.uitm.edu.my/id/eprint/1985/ |