## 1 Introduction  
Group intelligence is a significant concept in the field of artificial intelligence, and existing theories and application research on group intelligence have proven that group-based metaheuristic algorithms are a new method that can effectively solve optimization problems [1]. The swarm intelligence algorithm simulates the behavior of a biological population or natural phenomena, and a group of simple individuals follows specific interaction mechanisms to complete a given complex optimization problem. Faced with increasingly complex optimization problems, especially those involving continuous and discrete variables coexisting and multi-dimensional and nonlinear optimization problems, swarm intelligence algorithms exhibit advantages such as robustness, robustness, and economy [2]. The relevant theoretical achievements have been widely applied in path planning [3], machine learning [4], workshop scheduling [5], and optimization problems. Over the past half century, many intelligent optimization algorithms have emerged. [PERSON] et al. [6], inspired by the regularity of bird clustering activities, propose PSO (Particle Swarm Optimization). The WOA (Whale Optimization Algorithm) [7] simulated whale swarm search, encidrement, pursuit, and attack of prey to achieve optimization objectives. [PERSON] et al. [8] imitated the butterfly's foraging process and proposed BOA (ButterflyOptimization Algorithm). In addition, various emerging swarm intelligence algorithms, such as HHO (Harris Hawks Optimizer) [9], AEO (Artificial Ecosystem-based Optimization) [10], and AVOA (African Vultures Optimization Algorithm) [11], have been proposed successively and have attracted widespread attention. The Hippopotamus Optimization Algorithm (HO) simulated defense and evasion strategies against predators and performed location updates. It has the advantages of high accuracy, strong local search ability, and good practicality [12]. This algorithm still has significant research value in improving global search, enhancing local development capabilities, and avoiding local optima. This article proposes a hybrid Improved Hippopotamus Optimization Algorithm (IHO), which combines Latin hypercube sampling, Jaya algorithm's profit-seeking and harm-avoiding ideas, and three strategies of random crossover and sequential mutation to optimize the performance of IHO. These improve its global search capability and accelerate optimization convergence speed. Power prediction is an important component of the power generation plan in the power system and the foundation of the economic operation of the power system. The accuracy of the prediction plays a crucial role in the operation, maintenance, and planning of the entire power system [13]. The integration of diverse loads, such as large-scale distributed power sources and electric vehicle charging stations, has brought about greater load volatility, temporal variability, and randomness, further increasing the difficulty of prediction [14]. Traditional prediction methods, such as regression analysis [15], time series method [16], exponential smoothing method [17], and Kalman filtering method [18], have low prediction accuracy and are unable to adapt to the nonlinear characteristics of sequences. Considering the temporal and nonlinear characteristics of load sequences, scholars from various countries have conducted extensive in-depth research at present. Back Propagation (BP) neural network is one of the most widely used neural network models; however, it has the disadvantages of slow training speed and getting easily stuck in local minima [19]. The extreme learning machine (ELM), as a single hidden layer feedforward neural network, has fewer model parameters, significantly better learning speed than support vector machines (SVM) and traditional neural networks, and the advantages of high generalization ability and small prediction error [20]. Therefore, this article chooses the ELM as the basis for the prediction model.