Fangzhao Deng’s research while affiliated with State Grid Electric Power Research Institute and other places

This paper discusses the possibility of combining low-carbon intelligent power management technology with carbon emission accounting systems in the context of the pressing global climate change. The article emphasizes the crucial role of these technologies in promoting sustainable power system development. It introduces the basic principles of carbon emission accounting and its application in the electricity industry, highlighting how the integration of intelligent power management with emission accounting can help reduce carbon emissions. Furthermore, the paper provides insights into algorithm development and real case studies, demonstrating the effectiveness of these methods. In summary, this integrated approach supports a cleaner, more sustainable energy future.

The accuracy of electricity demand forecasting is closely related to the correctness of decision-making in the power system, ensuring stable energy supply. Stable energy supply is a necessary guarantee for socioeconomic development and normal human life. Accurate electricity demand forecasting can provide reliable guidance for electricity production and supply dispatch, improve the power system's supply quality, and ultimately enhance the security and cost-effectiveness of power grid operation, which is crucial for boosting economic and social benefits. Currently, research on electricity demand forecasting mainly focuses on the single-factor relationship between power consumption and economic growth, industrial development, etc., while neglecting the study of multiple influencing factors and considering different time dependencies. To address this challenge, we propose a transformer-based forecasting model that utilizes transformer networks and fully connected neural networks (FC) for electricity demand forecasting in different industries within a city. The model employs the encoder part of the transformer to capture the dependencies between different influencing factors and uses FC to capture time dependencies. We evaluate our approach on electricity demand forecasting datasets from multiple cities and industries using various metrics. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Overall, we provide a valuable framework in the field of electricity demand forecasting, which holds practical significance for stable power system operations.