Hybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting

Abstract

Accurate and consistent forecasting of regional wind power is essential for efficient scheduling and maximizing the utilization of renewable energy in the power grid. Medium-term PV and wind power forecasting play a vital role in various aspects of renewable energy planning and management. It provides valuable insights into future wind power generation trends, enabling effective resource allocation, grid integration, and energy market operations. This paper proposed a hybrid inception embedded deep neural network with ResNet architecture, Proposed model utilizes ResNet blocks and Inception modules to enhance feature extraction and attention mechanisms. Furthermore, The bidirectional weighted LSTM and GRU layers are used to further increase the model's ability to capture temporal dependencies and provide accurate power generation. The Time2Vec method is integrated to capture important complex and nonlinear temporal information and patterns in the time series data. Our model utilizes both convolutional and recurrent neural network components to learn and predict power generation with improved accuracy. This study employs Chinese State Grid PV and wind datasets and Natal wind datasets to determine the performance of our proposed IEDN-RNET model. The results of our study show that the proposed IEDN-RNET model outperforms other algorithms with a 12% lower Mean Absolute Error, 13% lower Root Mean Square Error, 19% lower Normalized Mean Absolute Error, 20% lower Normalized Root Mean Square Error, and higher R-Square and correlation coefficients.