## 1 Introduction
Recently, particular attention is being paid to SVR, random forest, and other improved time series methods [20; 21]. A considerable amount of literature about these methods has been published in various journals and websites. Traditional load forecasting is based on regional forecasting. With advanced metering infrastructure (AMI) being rolling out and the update of technology, targeted load forecasting, such as industrial load forecasting and residential load forecasting, has gradually attracted people's attention [18; 22; 23; 24]. Typical forecasting models tend to be not well adapted to this volatile problem. The different models of ANN, support vector machine (SVM), and time series forecasting techniques are used in this new direction of research. Aiming at the problem of variability residents' activities, the authors of a past paper [22] proposed a long short-term memory-based deep-learning forecasting framework with appliance consumption sequences to address such volatile problem. Similarly, in order to solve the factory industrial load forecasting, the authors of another work [23] developed a set of multiple linear regression models for an Italian factory that manufactures transformers. In view of the medium-term probabilistic forecasting of the electricity load of industrial companies, the authors of a past paper [24] suggested a novel inhomogeneous markov switching approach for the probabilistic forecasting of industrial companies' electricity loads. Multivariable input data can increase the accuracy of models; however, if too many parameters selected it can cause overfitting and a multicollinearity disaster. In recent years, researchers have investigated a variety of approaches to solve this problem. The authors of a past paper [10] discussed the least absolute shrinkage and selection operator (lasso), stepwise regression, regularized least-squares regression, principal component regression, and partial least-squares regression, which are some common and effective methods for solving multicollinearity. The optimal lag and number of layers of selection for LSTM model using genetic algorithm (GA) enabled proposed by previously [16] can prevent the overfitting problem. The authors of a past paper [25] present a methodology for probabilistic load forecasting that is based on lasso estimation; lasso is an effective method to avoid overfitting. A previous paper [26] raised the harm of multicollinearity when studying forecasts of commercial building electricity loads, and proposed tackling this problem by using principle component analysis (PCA). Short-term load forecasting has a high requirement for prediction accuracy, but the modern power environment is complex and variable, the factors affecting the load are more diversified, and the fluctuation of load affected by the environment is more obvious. Because the nonlinear relationship between load and characteristic variables presents a multidimensional and more complex trend, it is difficult for many algorithms to achieve accurate and efficient load forecasting based on multiple input variables. The group method of data handling (GMDH), also known as the polynomial neural network, is a self-organizing and inductive evolutionary algorithm. It fits complex nonlinear laws through the selection and weight adjustment of multilayer neurons [27; 28; 29; 30; 31]. Therefore, the input variables of the GMDH algorithm can be as comprehensive and extensive as possible, which is suitable for dealing with multidimensional and complex variables. GMDH is widely used in route planning, big data analysis, traffic flow prediction, and other fields. There are also many research studies on GMDH. Some studies have combined GMDH with other algorithms to improve the prediction accuracy, such as in a certain past paper [32], in which an neuro-fuzzy group method of data handling (NF-GMDH) network was combined with particle swarm optimization (PSO), the gravitational search algorithm (GSA), and a genetic algorithm (GA) to improve the forecasting accuracy.