Data Modeling of Energy Consumption in High Energy-Consuming Industries Based on Semi-Supervised Learning Algorithm

Inhalt:
The implementation of emission reduction measures in high-energy-consuming industries will increase the financial cost of enterprises in the short term and affect the business results of enterprises. The positive effects brought by the implementation of emission reduction measures by enterprises are sufficient to make up for the costs incurred by enterprises in taking emission reduction measures, thereby enhancing the market value of enterprises. To overcome the adverse effects of the volatility and uncertainty of energy consumption in high-energy-consuming industries on energy consumption, this paper proposes a cloud model load group dynamic regulation algorithm that takes into account delay compensation, and designs a load regulation with LA as the middleman Under the constraints of load adjustable margin, considering the delay in the aggregation process of high energy-consuming industry load groups, the LS-SVM method is used to make ultra-short-term predictions for user load groups in the aggregation delay interval. The real-time fluctuation of the load group uses the two-dimensional cloud task generator to dynamically compensate the control amount to improve the control effect. This solution integrates the load resources of the user group, realizes the consumption of energy consumption fluctuations, and optimizes the resource optimization on the demand side. The configuration has played a significant role in improving the balance of power supply and demand in the regional power grid and has played a role in ensuring the stability of the power grid.