SL-ANFIS-LSTM: A Structure Learnable Fuzzy Neural Network for Ultra-Short-Term PV Power Forecasting

As an important renewable energy source, photovoltaic (PV) power is widely used in power systems because of its clean, sustainable and increasingly mature technology, and ultra-short-term PV power forecasting is very important for the real-time dispatchment of power grids. However, PV power has strong periodicity and randomness, changes in weather factors such as illumination and temperature will significantly affect its power generation efficiency, which also brings challenges for PV power forecasting. In this paper, we propose a novel deep learning model called SL-ANFIS-LSTM for ultra-short-term PV power forecasting. SL-ANFIS-LSTM utilizes a structure learnable ANFIS (SL-ANFIS) module based on the adaptive neuro-fuzzy inference system (ANFIS) and Kolmogorov-Arnold networks (KAN) to characterize uncertain fuzzy factors such as weather and temperature, and a long short-term memory (LSTM) module to extract the temporal dependency. Besides, physical information is also introduced to correct illegal errors caused by changes in sunlight intensity. Experimental results show that the proposed model is superior to related models for ultra-short-term PV power forecasting.