Forecasting PV panel output using prophet time series machine learning model

Abstract

Due to climate change effects, the demand for renewable energy is growing immensely around the world. Photovoltaic (PV) panels are widely popular as a vital source of renewable energy all over the world as well as in Bangladesh. However, besides solar irradiance, the panel output is greatly affected by some of the weather parameters like temperature, humidity, wind, etc. Reliable forecasting of PV panel output is essential for capacity planning in advance to efficiently manage the energy distribution. This paper presents a method to forecast the PV panel output energy using a machine learning model, known as the Prophet Model used for a univariate time series forecasting. For this study, the PV panel generated data are collected from an outdoor experimental set-up throughout the full winter season in Bangladesh. Based on the data, forecasting of one-day-ahead PV panel short circuit current is done, and then the estimation of PV panel output energy is made. The results show the proposed forecasting method to be quite encouraging and reliable one while providing a higher coefficient of determination value with an average 0.9772 for one-day-ahead PV panel output energy forecasting.