| Title |
Hample filter based Short term Solar Forecasting using Facebook Prophet Library in Energy Harvested Wireless Sensor Network (EHWSN) |
| Authors |
(Vivek Kumar Verma) ; (Satya Sai Srikant) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.5.368 |
| Keywords |
Energy harvested wireless sensor network (EHWSN); Solar energy forecast; Prophet library; Hample filter |
| Abstract |
Energy harvesting prolongs the life of wireless sensor networks. Among all ambient sources of energy, solar energy is believed to be the most efficient in terms of energy density but fluctuates with time and location. The main objective of this study is to forecast solar energy for Energy Harvested Wireless Sensor Network (EHWSN) using the Facebook Prophet Library with and without a filter and to assess the model efficacy. The present study examined the models using National Renewal Energy Laboratory (NREL) one-minute sample data for June 2010 to 2016. The outliers were eliminated using a Hample filter with a standard deviation of 3 and a window size of 3. The forecast error was measured using the cross-validation feature of the Prophet Library. The Mean squared Error ( MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE ) values for the Prophet model were 292.81, 14.56, 19.37, and 17.11, respectively, while they were 92.56, 14.86, 14.86, and 17.10, respectively, for the Prophet model based on the Hample Filter. MAPE was reduced by 18.60 %, while the rest of the metrics changed significantly. The Prophet and Hample?Prophet give the best result regarding RMSE and accuracy compared with previous work based on the Ensemble Approach. |