Photovoltaic power forecasting: A dual-attention gated recurrent unit framework incorporating weather clustering and transfer learning strategy

Abstract

Accurate forecasting of photovoltaic power is essential in the integration, operation, and scheduling of hybrid energy systems. However, modeling for newly built photovoltaic sites is restricted by insufficient training data and computational burden. In this study, a weather clustering-based photovoltaic power forecasting framework incorporating attention mechanism and transfer learning strategy is proposed. By clustering historical days into multiple weather types, the gated recurrent unit-based encoder-decoders with dual-attention mechanism are designed to predict the photovoltaic power generations. The input attention and temporal attention mechanism are responsible for rebuilding input variables and context vectors of the encoder-decoder structure, respectively. Furthermore, a knowledge-transferring strategy, which focuses on establishing an alignment mapping module between the pre-trained structure and the target domain data, is designed for overcoming insufficient data of newly built sites. The data from the actual photovoltaic system are acquired to validate the proposed framework. The proposed forecasting model presents superior performance than other benchmark models, and the knowledge-transferring strategy not only addresses data shortage but also significantly accelerates the training process. With the introduction of knowledge-transferring, the maximum improvement in forecasting accuracy and training efficiency reaches 67.40% and 59.10%.