In the dynamic landscape of renewable energy, the integration of wind and solar power into the grid has become a critical focus for countries aiming to reduce carbon emissions and combat climate change. One of the key challenges in this transition is the accurate prediction of power output from these intermittent sources. Yuan Wang, a researcher at Inner Mongolia Electric Power Economic and Technological Research Institute, Inner Mongolia Electric Power (Group) Co., Ltd., has developed a groundbreaking method that promises to revolutionize the way we forecast wind and solar power generation.
Wang’s innovative approach, detailed in a recent study published in the journal ‘Applied Sciences’, addresses a significant hurdle in the industry: the lack of historical data in newly constructed wind and solar power stations. This scarcity of data often leads to declining prediction accuracy, a critical issue that can hinder the efficient integration of renewable energy into the grid.
The study introduces a transfer learning-based forecasting approach that incorporates sensitive meteorological feature selection and utilizes a Temporal Convolutional Network–Attention–Long Short-Term Memory (TCN-ATT-LSTM) model. This method not only enhances prediction accuracy but also significantly accelerates model training. By employing Spearman’s rank correlation, mutual information entropy, and Pearson correlation, Wang’s team investigates the relationship between various meteorological features and power output, ensuring that only the most relevant data is used in the forecasting model. This selective approach, as Wang explains, is crucial for improving model efficiency and accuracy: “By using evidence-based optimization theory, the model effectively refines the selection discrepancies from various evaluation metrics. This ensures that we are using the most relevant meteorological data, enhancing the efficiency and accuracy of our predictions.”
The TCN-ATT-LSTM network is pre-trained to extract common knowledge, and transfer learning is applied to fine-tune the model through network parameter adjustments. This adaptability allows the model to be quickly constructed for target wind and solar power stations with limited data, a significant advancement in the field.
The implications of this research are vast. For the renewable energy industry, accurate power forecasting is essential for grid stability and efficient energy management. Wang’s method not only improves forecasting accuracy for emerging wind and solar power stations with limited data but also has significant commercial impacts. Energy providers can better plan for energy distribution, reduce operational costs, and ensure a more stable and reliable energy supply.
The study’s findings were validated through its application to data from a projected wind and solar power station planned for a region in northwestern China. The results were compelling, demonstrating that the proposed method outperforms traditional deep learning models without transfer learning, as well as pre-trained TCN-ATT-LSTM and transfer learning with LSTM. This superior performance underscores the potential of Wang’s approach to shape future developments in the field.
As new wind and solar power stations collect more operational data, the target domain dataset for transfer learning will expand, further enhancing forecasting accuracy. This continuous improvement will be crucial for the renewable energy sector as it strives to meet growing energy demands while reducing reliance on fossil fuels.
Wang’s research, published in ‘Applied Sciences’, marks a significant step forward in the quest for more accurate and efficient renewable energy forecasting. As the world moves towards a more sustainable future, innovations like these will play a pivotal role in shaping the energy landscape.
