Research on power grid demand response forecasting model based on optimized Harris Hawk algorithm

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

With the rapid growth of power demand, load management and optimal dispatching of power grids have become particularly important. As an effective demand-side management method, demand response has become an important tool for power grid load regulation. However, accurate demand response forecasting models are crucial for the dispatching and load balancing of the power grid. Traditional demand response forecasting models have some problems, such as low accuracy and high computational complexity, so optimization algorithms are needed to improve the accuracy and efficiency of forecasting. In this paper, a grid demand response prediction model based on optimized Harris Hawk algorithm is proposed. Harris Hawk algorithm is a new swarm intelligence optimization algorithm, which can effectively deal with complex optimization problems. In order to improve its application effect in demand response forecasting, this paper introduces an optimization mechanism to improve the global search ability and local search ability of the algorithm, and combines historical power demand data, meteorological data and economic factors to construct a demand response forecasting model based on HHO. Compared with traditional prediction methods, the results show that the optimized HHO algorithm has significant advantages in prediction accuracy. On the selected test dataset, the Root Mean Square Error of the proposed model achieves a 12.8% relative reduction compared specifically to the original standard HHO baseline, while the Mean Absolute Percentage Error is significantly minimized. In addition, this paper also evaluates the stability and computational efficiency of the model, and the results show that the optimized HHO algorithm can maintain good prediction performance in different scenarios. This study provides a new idea for the accurate prediction of power grid demand response, and has strong practical application value, especially in high-load and large-scale power grid dispatching.

Links and sources

• PDF from original source

Comments