Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and , one of the key challenges facing solar energy forecasting is choosing the right inputs achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be to build different forecasting models. However, operators of grid-connected PV farms do not have full sets of data available to them especially over an extended period of time as required key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, research reported here considered these two main approaches of building prediction models and their performance when utilizing structural, time-series, and hybrid methods for data input. years of PV power generation data (of an actual farm) as well as historical weather data (of the location) with several key variables were collected and utilized to build and test six prediction . Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have prediction accuracy depending on the input method used to build the model: ANN models better than the MR regardless of the input method used. The hybrid input method results better prediction accuracy for both MR and ANN techniques, while using the time-series method in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data does impact forecasting accuracy negatively especially for the structural approach.