Short-term load forecasting using artificial neural network / Suhana Shaari
This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the A...
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| Format: | Thesis |
| Language: | en |
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2012
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| Online Access: | https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf https://ir.uitm.edu.my/id/eprint/84792/ |
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| _version_ | 1833078567133511680 |
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| author | Shaari, Suhana |
| author_facet | Shaari, Suhana |
| author_sort | Shaari, Suhana |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in term of forecasting error. Historical load data and temperature observations for the year 2006 - 2010 obtained from the Australian Energy Market Operator (AEMO) & Bereau of Meteordology (BOM) for Sydney/NSW. The inputs used were the hourly load demand for the full day (24 hours), the weather, humidity and holiday for the state.-The network trained over 4 year's data. A mean average percent error (MAPE) of 1.99% was achieved when the trained network was tested on one year data. |
| format | Thesis |
| id | my.uitm.ir-84792 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2012 |
| record_format | eprints |
| spelling | my.uitm.ir-847922024-01-15T09:00:20Z https://ir.uitm.edu.my/id/eprint/84792/ Short-term load forecasting using artificial neural network / Suhana Shaari Shaari, Suhana This thesis presents a neural network based approach for short-term load forecasting that uses the most correlated weather data for training and testing the neural network of weather data determines the input parameters of the neural networks. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in term of forecasting error. Historical load data and temperature observations for the year 2006 - 2010 obtained from the Australian Energy Market Operator (AEMO) & Bereau of Meteordology (BOM) for Sydney/NSW. The inputs used were the hourly load demand for the full day (24 hours), the weather, humidity and holiday for the state.-The network trained over 4 year's data. A mean average percent error (MAPE) of 1.99% was achieved when the trained network was tested on one year data. 2012 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf Short-term load forecasting using artificial neural network / Suhana Shaari. (2012) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). |
| spellingShingle | Shaari, Suhana Short-term load forecasting using artificial neural network / Suhana Shaari |
| title | Short-term load forecasting using artificial neural network / Suhana Shaari |
| title_full | Short-term load forecasting using artificial neural network / Suhana Shaari |
| title_fullStr | Short-term load forecasting using artificial neural network / Suhana Shaari |
| title_full_unstemmed | Short-term load forecasting using artificial neural network / Suhana Shaari |
| title_short | Short-term load forecasting using artificial neural network / Suhana Shaari |
| title_sort | short-term load forecasting using artificial neural network / suhana shaari |
| url | https://ir.uitm.edu.my/id/eprint/84792/1/84792.pdf https://ir.uitm.edu.my/id/eprint/84792/ |
| url_provider | http://ir.uitm.edu.my/ |