Solar irradiance forecasting using deep learning approaches
| dc.contributor.author | Fernando, W.L.M. | |
| dc.contributor.author | Jayalath, W.M.W.S. | |
| dc.contributor.author | Kanagasundaram, A. | |
| dc.contributor.author | Valluvan, R. | |
| dc.date.accessioned | 2022-03-11T02:23:30Z | |
| dc.date.accessioned | 2022-06-27T10:02:08Z | |
| dc.date.available | 2022-03-11T02:23:30Z | |
| dc.date.available | 2022-06-27T10:02:08Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The purpose of this study is to come up with a most accurate model for predicting the Solar photovoltaic (PV) power generation and Solar irradiance. For this study, the data is collected from Faculty of Engineering, University of Jaffa solar measuring station. In this paper, deep learning based univariate long short-term memory (LSTM) approach is introduced to predict the Solar irradiance. A univariate LSTM and auto regressive integrated moving average (ARIMA) based time series approaches are compared. Both models are evaluated using root-mean-square error (RMSE). This study suggests that univariate LSTM approach performs well over ARIMA approach. | en_US |
| dc.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/5597 | |
| dc.language.iso | en | en_US |
| dc.subject | Solar photovoltaic | en_US |
| dc.subject | Solar irradiance | en_US |
| dc.subject | Prediction model | en_US |
| dc.subject | time series | en_US |
| dc.subject | ARIMA | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | LSTM | en_US |
| dc.title | Solar irradiance forecasting using deep learning approaches | en_US |
| dc.type | Article | en_US |