A medium and long-term carbon emission forecasting method for provincial power grid
The development of low-carbon electric power industry is a key way to deal with the greenhouse effect and lower the PM2.5. When integrated into power grid, renewable energy can efficiently cut down the output of the thermal power and reduce the carbon emission of the grid significantly. However, it lacks a more comprehensive and systematic approach to forecast a medium and long-term carbon emission for provincial power grid. Thus, in this paper, life cycle assessment (LCA) method and gray prediction model are used to forecast the medium and long-term carbon emission. In addition, the carbon intensity of provincial power grid is evaluated based on historical data such as electricity consumption, gross domestic product (GDP), carbon emission coefficient. Taking Jiangxi power grid as the example, the results indicate that the proposed method is of good accuracy and the integration of renewable energy can effectively reduce the carbon emission and the carbon intensity.
Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques
Since the magnificent goal of Peak Carbon Dioxide Emissions and Carbon Neutrality was put forward in 2020, carbon emission reduction has attracted unprecedented attention. The power industry must fulfill its carbon emission reduction obligations as soon as possible. Thermal power plants are the main source of carbon emissions in the power industry, so finding out the key influencing factors of thermal-power-plant carbon emission and making accurate predictions are important measures to promote the low-carbon development of the power industry. Although some precise models have been proposed, most power plants cannot obtain all the parameters required by the precise models in the actual production practice, which limits their application. Machine learning technology accepts numerical data as input and establishes the mapping relationship between variables automatically, which results in loose requirements on data. This paper summarizes several key influencing factors of carbon dioxide emissions of thermal power plants that are easy to observe and establishes a prediction model of carbon dioxide emissions of thermal power plants based on eXtreme Gradient Boosting. In addition, we compare our method with two machine learning methods proposed in previous research and obtain a satisfactory result.
Technology Transfer out of Germany after 1945
Technology Transfer Out of Germany studies the movement of technology and scientists between East Germany and the Soviet Union, and West Germany and the Western Allies, using documented examples and case studies, and asks whether the confiscation of documents, equipment and scientists can really be considered to be a form of 'intellectual reparation.'