Abstract
The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.
Highlights
As photovoltaic (PV) power harvesting becomes more affordable, PV systems are increasingly being integrated into and used for existing power systems
In Suresh et al [30], a manually tuned convolutional neural network (CNN) model was used for PV power forecasting, which consisted of four convolutional layers
These historical data were grouped in terms of the CNN classifications for five weather types: rain, heavy cloudy, cloudy, light cloudy, and sunny
Summary
As photovoltaic (PV) power harvesting becomes more affordable, PV systems are increasingly being integrated into and used for existing power systems. In Suresh et al [30], a manually tuned CNN model was used for PV power forecasting, which consisted of four convolutional layers Those convolutional layers were assigned for each input variable and fed to the max-pooling layer. The proposed method uses a CNN optimized with an SSA to seamlessly generate the forecasting model without the requirement of time-consuming trial and error. The state-of-the-art features of the proposed method are as follows: (1) simple arrangement of input variables that moderate features and training datasets for an accurate day-ahead forecasting result, (2) a modified state-of-the-art forecasting algorithm to produce a fine-tuned CNN model that requires no time-consuming trial and error, and (3) consistent accuracy in short-term PV power forecasting from day-ahead to three-days-ahead forecasting windows.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
