Artificial Neural Network Based Short-Term Load Forecasting For the Ethiopian Electric and Power Corpora Tion-(Eepco)
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Tadesse, Mekonnen | |
| dc.date.accessioned | 2020-06-10T10:08:16Z | |
| dc.date.accessioned | 2023-11-18T12:45:42Z | |
| dc.date.available | 2020-06-10T10:08:16Z | |
| dc.date.available | 2023-11-18T12:45:42Z | |
| dc.date.issued | 2004-07 | |
| dc.description.abstract | Load forecasting has become, in recent years, one of the major areas of research. Most traditional forecasting and artificial intelligence researches have tried out this task. Artificial neural networks (ANNs) have lately received much attention, and successful experiments and practical tests have been reported. This work studies the applicability of this kind of model procrastinating.The multi-layered feed-forward neural network, that are capable of representing nonlinear fictional mappings between inputs and outputs was used to model the short term load to recast for the Ethiopian electric and power corporation (EEPCO). The network was trained with the error back-propagation method. Two models were studied in this whole process. The first one is forecasting the load one hour ahead and secondly the daily peak load forecast.The test results, based on historical demand, indicates that this methodology is capable of providing accurate forecasts with 1.1 % and 1.3 % average absolute forecast errors for the hourly and daily peak load forecasts respectively. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21511 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Artificial Neural Network Based Short-Term Load Forecasting For the Ethiopian Electric and Power Corpora Tion-(Eepco) | en_US |
| dc.type | Thesis | en_US |