# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 5 Conclusions  
This paper describes the basic concept of MWP and analyzes the indicators of MWP. On this basis, a daily gas load prediction model based on MWP-DLP was established. The gas data of a city (daily gas load, average temperature, solar radiation value, wind value) were selected as the research object. The MWP-DLP model was solved, and the  
Figure 11: Relative error between the predicted and actual values. Figure 10: Curves of the predicted and actual values versus the average temperature. results and errors were analyzed to verify the accuracy of the model. Finally, the predictive performance of the model was analyzed. The main conclusions are as follows:  
1. In the research of daily gas load prediction, the influence of the dynamic change of building foundation temperature on daily gas load should be fully considered, which becomes the key breakthrough point to improve the prediction accuracy. Through in-depth analysis of the internal relationship between temperature fluctuation and gas consumption, the coefficients of key environmental factors, such as cumulative temperature, solar radiation, and wind power, were carefully corrected, and the MWP-DLP model was successfully constructed. 2. In order to obtain the optimal solution of MWP-DLP model, the genetic algorithm was used for deep optimization. The genetic algorithm, with its powerful global search ability, is constantly iteratively optimized in complex solution space. The MWP-DLP model was successfully solved after 1100 carefully set iterations. In order to verify the reliability of the model, city data with typical climate characteristics and energy consumption patterns were selected as control samples. The city's geographical environment, climatic conditions, and urban energy consumption characteristics made it an ideal case to test the performance of the model. 3. By analyzing the data of a city in detail, the daily gas load predicted by the MWP-DLP model was compared with the actual value. The results show that the variation trends of the two were basically consistent, which fully indicates that the strategy of considering variable building base temperature and modifying cumulative temperature, solar radiation, and wind coefficient can significantly improve the prediction accuracy of daily gas load. In the error analysis, the maximum error value of the model was \(12\times 10^{4}\) m\({}^{3}\), the maximum relative error was \(-8.2\%\), and the mean absolute percentage error (MAPE) was \(2.68\%\). Such a low error range strongly proves that the established MWP-DLP model not only has good feasibility and can run effectively in practical applications but also has high effectiveness and can provide a reliable decision-making basis for gas supply management. 4. The prediction model constructed in this study not only realizes the high-precision prediction of short-term gas load but also provides technical support for the adjustment of energy structure. The quantitative analysis of the dynamic relationship between meteorological factors and load can optimize the cooperative scheduling of natural gas and renewable energy and promote the cross-season storage and efficient utilization of intermittent energy, such as wind power and photovoltaic. In view of the increase in baseline load caused by urban population growth, the model can include slow-changing variables such as population and industry in the future to provide cross-time scale support for gas infrastructure planning and alleviate the contradiction between supply and demand of limited resources. In the long run, the model's accurate capture of extreme weather load mutations also lays a foundation for the assessment of peak load balancing capacity of alternative energy sources, such as hydrogen energy, and promotes the transformation of the energy system to a diversified and sustainable one. 5. Although this study improves the prediction accuracy by combining weather variables and genetic algorithm, there are still limitations in the characterization of complex load models by linear models.