Multi-dimensional Collaborative Checking of Power Consumption of Purchasing Agent Users Based on ISSA Optimization Algorithm

With the steady progress of the proxy electricity purchase business, electricity consumption prediction plays a crucial role in the operation of smart grids. Currently, most research focuses on improving the accuracy and reliability of prediction results through algorithms. Still, these methods need more comprehensive consideration and accurate verification of multidimensional factors in the power system. Therefore, predicting the electricity consumption of proxy electricity purchasing users from multiple dimensions comprehensively is one of the problems faced in the proxy electricity purchasing business. This article proposes a verification method for user electricity consumption prediction results considering multi-dimensional collaboration. Firstly, this article adopts a deviation probability distribution model to analyze the effective deviation distribution of each dimension (region, industry, voltage level) and identify the effective deviations of each dimension. Secondly, to minimize error, the ISSA optimization algorithm is used to optimize the ratio of multidimensional weights, and a multifaceted collaborative verification model is constructed by combining predicted values and weight values for weighting. Finally, error indicators are selected to evaluate the expected values after multi-dimensional verification. This article validates the above algorithm by combining the electricity consumption of proxy electricity-purchasing users in a particular province. The results show that the multi-dimensional collaborative verification method based on the ISSA optimization algorithm reduces the average absolute error index by 51.9%, 23.4%, and 19.1% compared to the industry, regional, and voltage level dimensions, respectively. The root mean square error index showed a reduction of 40.0%, 15.0%, and 8.6% compared to the industry, regional, and voltage level dimensions, respectively, indicating good generalization ability.