Research on Intelligent System of Load Forecasting and Scheduling Optimization Based on Machine Learning in Smart Power Grid

The system integrates a variety of machine learning algorithms, including but not limited to regression analysis, neural networks and support vector machines to improve the accuracy of load prediction. By learning from historical load data and pattern recognition, the system is able to predict the power demand in the future period. This paper further designs a set of power optimization scheduling algorithm. The algorithm considers multiple factors such as power supply and demand balance, power generation cost minimization, environmental impact, etc. By adjusting power generation plan and power distribution strategy in real time, the economic operation and environmental friendliness of the power system are realized. In addition, the algorithm also introduces smart contracts and blockchain technology, enhancing the transparency and security of scheduling decisions. The experimental results show that compared with the traditional method, the load prediction based on machine learning significantly improves the prediction accuracy and reduces the prediction error. At the same time, the optimized scheduling algorithm effectively reduces the operating cost of the power grid and improves the energy utilization efficiency.