# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 1 Introduction
### Literature Review  
Domestic and foreign scholars have put forward some methods or means for gas load forecasting. Commonly used methods include traditional statistical methods, artificial intelligence methods, and so on. The traditional statistical method of gas load forecasting is to build a forecasting model based on the time series characteristics of historical data and the statistical relationship between variables. Traditional statistical methods include exponential smoothing [16; 17], regression analysis [18], autoregressive moving average model [19; 20], and gray analysis [21; 22]. The results show that the traditional statistical method has certain application value when the data amount is small and the data rule is relatively simple. However, in the face of complex and variable gas load data, there are certain limitations. The regression analysis method should introduce appropriate independent variables in the prediction, so that the errors will be added to the corresponding dependent variables, resulting in large errors [10]. The smoothing index method is only suitable for forecasting in a short period [23; 24; 25]. In the actual gas load forecasting process, the forecasting ability and accuracy of traditional statistical methods are inferior to that of artificial intelligence methods. The artificial intelligence method of gas load forecasting is to use computer algorithms to learn and analyze a large amount of historical data, so as to establish a model that can accurately predict the future gas load. Artificial intelligence methods such as the fuzzy logic method [26; 27], artificial neural network method [28; 29], and support vector machine method [30] have been widely used. [PERSON] and [PERSON] [31] used the genetic algorithm to design evolutionary regression neural network to predict the half-hour power load. [PERSON] et al. [32] adopted a mixed model combining fruit fly optimization algorithm (FFOA), simulated annealing algorithm (SA), cross factor (CF), and support vector machine (SVM) to forecast gas short-term load, which had higher accuracy in gas short-term load prediction. Although artificial neural network method has good approximation effect and fast calculation speed, due to the inherent limitations of artificial neural network as a black box model, forecasters can only observe the mapping results from input data to output prediction and cannot analyze the system prediction process [33; 34], which has shortcomings such as local optimal solution, which limits its practical engineering application in daily natural gas load prediction. In terms of the research progress of artificial neural network, [PERSON] et al. [35] proposed a combined prediction model of gas load based on machine learning, which controlled the average absolute percentage error of gas load prediction within 1.9%. [PERSON] et al. [9] used the generated simulation data and historical data to form an enhanced dataset to train the model, reducing the daily gas load forecasting error to less than 7%. Scholars generally focus on improving the algorithm to improve the prediction accuracy of gas load but pay less attention to the factors that affect the change of gas load and lead to the change of user demand. With the deepening of the research, more and more scholars began to consider the influence of various influencing factors on gas load. In terms of weather factors, more and more research has begun to focus on temperature variables. [PERSON] et al. [32] constructed a mixed model of support vector machine optimized by chaotic firefly-simulated annealing-fuzzy firefly optimization algorithm to predict the short-term load of urban gas and discussed the influence of temperature type on the prediction results. [PERSON] et al.