Based on the daily maximum power load values in Jingzhou, Jingmen, Yichang, Xianning and Suizhou areas of Hubei Province from 2008 to 2019 and the meteorological data of national meteorological observation stations in the same period, the relationships between the change rate of maximum meteorological load (Lpm), four comfort indexes such as temperature and humidity index (I), meteorological sensitive load index (MSLI), human comfort (ET) and somatosensory temperature index (Te) and temperature were analyzed. The daily maximum power load forecasting models were established based on the above four comfort indexes by using multiple regression and BP neural network method. The results show that the Lpm was positively correlated with temperature and the above four comfort indexes in summer, negatively correlated in winter, and the correlation was significantly higher in summer than in winter. The changes of the above four comfort indexes integrating temperature, humidity and wind speed could cause the change of the Lpm, and this change was more obvious in summer, especially in July and August. The errors of BP neural network model and multiple regression model were basically controlled within the requirements of the power department. The prediction effect of BP neural network was better than that of multiple regression. In the later business application, it was suggested to select ET index in Jingmen and Xianning, and four indexes in other cities can be used.
Based on the detailed information on transmission line lightning strike and meteorological elements data in 6 hours before transmission line lightning strikes from 1990 to 2017 in Shijiazhuang of Hebei Province, the statistical methods such as occurrence probability, occurrence frequency, linear trend and fluctuation amplitude were used to analyze occurrence regularity of transmission line lightning strikes, variation of timing wind, pressure, relative humidity, air temperature and ground temperature in 6 hours before transmission line lightning strikes. By defining the cumulative occurrence frequency of transmission line lightning strikes, the meteorological element indicators of occurrence of transmission line lightning strikes were determined, and the accuracy of level prediction of transmission line lightning strike accidents in Jingxing county of Shijiazhuang on 9 August 2018 was tested by comparing the EC numerical prediction with the automatic station data. The results show that the transmission line lightning strike accidents increased significantly and there were three peaks in Shijiazhuang in recent years, occurring mainly from afternoon to morning in summer, in August there was the highest probability, and in a day it was highest from 03:00 BST to 04:00 BST. In addition, when there was easterly wind, air pressure and relative humidity were rising, or when air temperature and land surface temperature were dropping, transmission line lightning strike accidents occurred frequently. Within 6 hours, when air pressure rose by 0.0-2.0 hPa, air humidity increased by 0-14%, air temperature dropped by 0-3.0 ℃, land surface temperature dropped by 0-6.5 ℃, and with the east wind as the central wind direction, the timing wind direction was within the range of 90°, the occurrence regularity and meteorological indicators of lightning strikes on transmission lines were well predicted and tested on 9 August 2018, which had certain guiding significance for preventing lightning strike accidents.
Based on the lightning monitoring data of 33 lightning location monitoring stations and real-time lightning disaster data of 48 counties in Qinghai Province from 2010 to 2019, the spatial distribution and risk zoning of lightning disasters in Qinghai Province were analyzed by using mathematical statistics and ArcGIS spatial analysis method. The results show that the regions with more lightning frequency and strong positive and negative lightning current intensity were mainly distributed in the central and eastern part of Qinghai Province, while the areas with high value of thunderstorm days were mainly distributed in the Qilian Mountain and the southern part of Qinghai Province. The lightning disaster risk presented obvious regional differentiation in Qinghai Province. The high-risk regions were mainly located in Kunlun Mountains, Qilian Mountains, Nyainqentanglha Mountains, Bayan Har Mountains and Anyemaqen Snowy Mountains, as well as part of the southern grazing area of Qinghai Province. The northwest of Qaidam Basin, the southeast pastoral area of Qinghai Province and some areas around Qinghai Lake were medium-risk areas. The risk level in most of the eastern agricultural area, part of Qaidam Basin, Wudaoliang and Tuotuo River area was relatively lower.
Based on observed total solar radiation, air temperature, relative humidity and air pressure data at representative photovoltaic power stations of Gansu Province, total solar radiation data forecasted by WRF model, and total cloud cover products from FY satellite in 2019, the correlation between total solar radiation and meteorological factors was analyzed, and the prediction ability of WRF model was evaluated, firstly. And on this basis the errors of short-time solar radiation forecast were corrected. The results show that the atmospheric transmissivity was positively correlated with air temperature, and the correlation coefficient was 0.61, while it was negatively correlated with relative humidity, air pressure and total cloud cover, and the correlation coefficients were -0.44, -0.31 and -0.81 in turn. The contribution of total cloud cover to solar radiation attenuation was the most, followed by relative humidity. The deviation of solar radiation forecasted by WRF model was bigger, and the monthly distribution of forecast errors appeared ‘single peak’ pattern, the forecast errors was the biggest in June. The root mean square error (RMSE) of solar radiation forecast was the smallest in winter (45.63 W·m -2) and the biggest in summer (240.4 W·m-2). The forecast ability of WRF model was better on sunny days or partly cloudy days, while it was worse on cloudy days. The forecast errors mainly came from phase bias and system bias. The correction effect of solar radiation forecast considering cloud cover was significant, the RMSE of solar radiation forecast after correction sharply decreased by 101-216.4 W·m-2 on cloudy days, the average absolute error decreased by 59.5-173.07 W·m-2, and the RMSE decreased by 1.92-64.23 W·m-2 in summer with the maximum error.
Based on CLDAS grid temperature data from National Meteorological Information Center of China, SCMOC grid temperature forecast data from Central Meteorological Observatory of China and temperature observation data at weather stations of Shanxi Province, the applicability of CLDAS temperature in Shanxi Province was evaluated comprehensively by using non-independence test method. And on this basis, based on CLDAS grid temperature data, the objective correction of SCMOC temperature forecast field was studied by using the sliding training period scheme. The results are as follows: (1) The complex terrain in Shanxi Province had a certain influence on the accuracy of CLDAS temperature, and the maximum temperature of CLDAS exhibited a better accuracy than the minimum temperature of CLDAS, which indicated that the influence of terrain on deviation of the minimum temperature was more significant, and the deviation of the minimum temperature in high altitude areas was negative generally, while that in low altitude areas was positive. (2) The deviation of CLDAS grid temperature had a continuity of time in space. After the simple deviation correction, the accuracy of the maximum and minimum temperature of CLDAS promoted by 1.1% and 9.7%, respectively, the revised temperatures were more consistent with observation. (3) Based on improved CLDAS grid temperature, the accuracy rate of SCMOC temperature forecast improved significantly by using the sliding deviation correction scheme. Compared to SCMOC, the accuracy rate of the 24-hour maximum and minimum temperature forecast in Shanxi Province in 2019 respectively increased by 2.7% and 4.7% after the sliding deviation correction. The quality of short-term temperature forecast after the sliding deviation correction had greatly improved, and it was superior to the subjective forecast of forecasters.
Based on daily gas load and meteorological observation data during heating period in Xi’an of Shaanxi Province from 15 November 2009 to 14 March 2019, the variation characteristics of gas load in heating period, holidays and weekends were analyzed. The significant influence factors on gas load were selected by using correlation analysis. And on this basis the daily forecast model of gas load in heating period was established by using multiple linear regression method, then the forecast model was tested. The results show that the natural gas consumption during heating period gradually increased in Xi’an in past 10 years, the daily gas load presented a single-peak pattern change, and the peak appeared in January. The weekend and holidays effects of gas load were obvious during heating period, the gas consumption on weekend and holiday was less than that on working days, and the longer holiday was, the less gas load was. The gas load was significantly and positively correlated with gas load on previous day, while that was significantly and negatively correlated with meteorological factors of the maximum and minimum temperature, mean temperature and human body comfortable degree, and the correlation between heating gas load separated from actual gas load and meteorological factors obviously improved. Based on the above five influence factors, the dynamic forecast model of daily heating gas load was established by using multiple linear regression method. Upon inspection, the average relative error of the model was 3.4%, and the model was more stable in rush hours of using gas, the average relative error was 2.77%, which could meet gas dispatch needs of natural gas companies.
Based on natural gas consumption, ground conventional meteorological observation data in Beijing in heating season from 2002 to 2018, as well as yearly social statistical information, the inter-annual variation characteristics of natural gas consumption in heating season in Beijing and its impact factors were analyzed by using empirical mode decomposition (EMD) and correlation analysis methods. And on this basis the forecast model of natural gas consumption in heating season was established by using back propagation (BP) neural network method, further the model was tested and evaluated. The results are as follows: (1) The natural gas consumption increased persistently in heating season from 2002 to 2018 in Beijing, and it was decomposed better into social and meteorological consumptions by EMD, which reflected long-term variation trend and short-term fluctuation of natural gas consumption, respectively. (2) The social consumption of natural gas in heating season had significantly positive correlation with GDP, intensive heating supply area and resident population number in Beijing. The meteorological consumption had significantly negative correlation with air temperature and negative accumulative temperature, while it was significantly positive correlated with precipitation and persistent low-temperature days. In heating season, when the air temperature was obviously lower or the continuous low-temperature and strong snowfall processes appeared, the meteorological consumption of natural gas would increase sharply. (3) The forecast model of gas consumption in heating season based on EMD_BP method had better prediction effect in Beijing, the average relative error was 5.6%, especially the model could predict accurately the peak and valley change of meteorological consumption of gas, which could provide scientific reference to a certain extent for energy planning and regulating.
©2018 Journal of Arid Meteorology
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