A Short-Term Forecasting Algorithm for Electricity Load Consumption of Energy Generating Plants Under Dual Carbon Goal ConstraintsSubtitle

Conventional short-term forecasting algorithms for power load consumption mainly use the DNN (Deep Neural Network) deep neural network model to generate variable forecasting curves, which is vulnerable to the impact of non-stationary load sequence changes, resulting in poor forecasting performance indicators. Therefore, considering the constraints of dual carbon goals, a short-term forecasting algorithm for power load consumption of energy power plants is proposed. That is to say, the training samples of short-term forecasting of power load are constructed considering the constraints of dual carbon goals, and the optimization process of short-term forecasting algorithm of power load consumption of energy power plants is designed, so as to realize short-term forecasting of power load consumption. The experimental results show that the designed short-term forecasting algorithm for power load consumption of energy power plants considering dual carbon target constraints has better forecasting indicators, better forecasting effect, reliability and certain application value, and has made certain contributions to improving the power supply and distribution efficiency of energy power plants and reducing comprehensive losses.