Forecasting Of Renewable Energy and Load Using Sliding Window and Neural Network Approach for Microgrid
| dc.contributor.author | Kangujam, Anelis | |
| dc.contributor.supervisor | Basak, Prasenjit | |
| dc.contributor.supervisor | Kaushal, Jitender | |
| dc.date.accessioned | 2019-09-23T11:08:41Z | |
| dc.date.available | 2019-09-23T11:08:41Z | |
| dc.date.issued | 2019-09-23 | |
| dc.description.abstract | This dissertation presents the study of application of Sliding Window Approach for forecasting. The past data can be utilized for predicting the future data. The data from 18th to 31st January and from 25th to 31st of January have been considered to forecast the data on 1st Feb. The current year’s variation throughout the week is being matched with that of the previous year by using the mean of Sliding Window Approach and the best window is selected for forecasting. The selected window and the current year’s weekly variations are used for the purpose of forecasting. The first objective of the work is to study the application of Sliding Window Approach for forecasting and the second objective is to propose a Sliding Window based algorithm for forecasting of data of Patiala in India using Matlab. The third objective is to compare the method of forecasting. The result for both the methods is compared and it is found satisfactory. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10266/5830 | |
| dc.language.iso | en | en_US |
| dc.publisher | Thapar Institute of Engineering and Technology | en_US |
| dc.subject | Sliding Window Approach (SWA) | en_US |
| dc.subject | Artificial Neural Network (ANN) | en_US |
| dc.subject | Mean Squared Error (MSE) | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | Microgrid | en_US |
| dc.title | Forecasting Of Renewable Energy and Load Using Sliding Window and Neural Network Approach for Microgrid | en_US |
| dc.type | Thesis | en_US |