A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Abstract

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

Research on the Automatically Independent Controlling Algorithm of the Grid-Connected PV Inverter Jointly Considering Dynamic Load Features and Active LVRT Capability

“…Just like a short circuit over a transmitting, a large-disturbance rotor angle or transient stability depends on the ability of the PV system to keep the synchronism when a serious disturbance happens. The rotor angle of a generator is as the following swing equation [33]: (1) In expression (1), M symbolizes inertia constant, 𝛿 means the rotor angle, 𝑃 # represents the mechanical input, and 𝑃 $ is an electrical output. Therefore, on the one hand, a generator will decelerate once 𝑃 %& increases, and 𝑃 ' is a fixed value; on the other hand, while 𝑃 ' is a fixed value and 𝑃 %& increases before a fault or after clearing the wrong action, the generator will accelerate.…”

“…In recent years, PV generation has been growing fast. It is necessary to address the negative effect of the high permeability PV on the grid [1] for improvement. Stability is a vital problem in a PV system [2].…”

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

“…Additionally, for more study about the prediction for PVs, refs. [78][79][80][81][82][83][84][85][86][87] can be studied.…”

Short-term day-ahead photovoltaic output forecasting using PCA-SFLA-GRNN algorithm

“…A novel hybrid method based on maximum overlap discrete wavelet transform is introduced by Haque et al (2013), where SARIMA and random vector functional link (RVFL) neural network was hybridized for PV power forecasting. A fuzzy clustering of atmospheric turbidity, solar irradiance, and relative humidity using improved fuzzy c-means clustering (IFCM) technique was done by Jinpeng et al (2022). Radial basis function (RBF) and genetic algorithm programming system (GAPS) were proposed for improving prediction accuracy.…”