earticle

영어

Nowadays, renewable energy resources such as Photovoltaic (PV) is one of the convenient ways to integrate it into the distributed grid to fulfill the huge energy demands without burning costly and pollutant fossil fuels. Researchers have been contributing from various aspects to develop accurate PV-power forecasting methods however further improvements are needed for an effective power management system. Therefore, in this work, we propose an attention-based deep learning (DL) model (PV-ANet) for short-term PV-power forecasting. The proposed system mainly consists of three modules. First, data from an actual PV power plant is acquired and preprocessed to remove outliers and normalized for efficient processing. Next, the PV-ANet model is developed, which is consisting of an encoder and decoder modules. The encoder encodes the input attributes via stack conventional and attention layer. While the decoder part contains the normalization and series of the dense layers to expends the encoded features into optimal features and generate one hour ahead forecast. Finally, the proposed model is evaluated via standard error metrics including MSE, MAE, and RMSE and achieved the lowest errors rates compared to state-of-the-art methods.