A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

Abstract

Accurate photovoltaic (PV) power forecasting allows for better integration and management of renewable energy sources, which can help to reduce our dependence on finite fossil fuels, drive energy transitions and climate change mitigation, and thus promote the sustainable development of renewable energy sources. A convolutional neural network (CNN) forecasting method with a two-input, two-scale parallel cascade structure is proposed for ultra-short-term PV power forecasting tasks. The dual-input pattern of the model is constructed by integrating the weather variables and the historical power so as to convey finer information about the interaction between the weather variables and the PV power to the model; the design of the two-branch, two-scale CNN model architecture realizes in-depth fusion of the PV system data with the CNN’s feature extraction mechanism. Each branch introduces an attention mechanism (AM) that focuses on the degree of influence between elements within the historical power sequence and the degree of influence of each meteorological variable on the historical power sequence, respectively. Actual operational data from three PV plants under different meteorological conditions are used. Compared with the baseline model, the proposed model shows a better forecasting performance, which provides a new idea for deep-learning-based PV power forecasting techniques, as well as important technical support for a high percentage of PV energy to be connected to the grid, thus promoting the sustainable development of renewable energy.