| Author | Danupol Wetchasirikul |
| Call Number | AIT Thesis no.ET-17-06 |
| Subject(s) | Wind forecasting--Computer programs
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy |
| Publisher | Asian Institute of Technology |
| Abstract | Intermittent renewable power generation may require to unplanned capacity of fossil fuel power generation sources which may be slow to respond to unexpected change in net load demand. The amount of power generated from wind turbine depends on the speed of wind at a particular location. Therefore, the accuracy of the forecasting of wind speed is necessary for short term planning and real time operation of a power system. Deep Learning is one of the latest approaches in the field of Machine Learning and Artificial Neural Networks study. The artificial neural network is widely used in conjunction with big data analysis or data mining study. Deep Neural Network is the artificial neural network that is constructed by using deep learning definition, architecture and algorithm. This thesis proposes a Deep Learning Network double hidden layers to hourly forecast the wind speed which is an improvement over the Shallow Neural Network. In summary, the test results of hourly wind speed forecasting based on three years of wind speed data from Asian Institute of Technology indicate that Deep Neural Network perform better than the shallow architecture using three years actual hourly wind speed data of Asian Institute of Technology. |
| Year | 2017 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy Technology (ET) |
| Chairperson(s) | Weerakorn Ongsakul |
| Examination Committee(s) | Singh, Jai Govind;Dhakal, Shobhakar; |
| Scholarship Donor(s) | The Bangchak Corporation Public Company Limited, Thailand |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2017 |