Beijing Electric Power Load and Its Relation with Meteorological Factors in Summer

Power security with stability is essential for normal operations of modern cities which guarantee industrial productions, communication, transportations, daily lives and so on. For the specificities of modern grid (electric power system), a local accident can spread to the entire electric grid instantaneously, and usually results in huge economic losses. The abnormal increase of power load can often cause an accident for the power grid. The power grid of Beijing is a typical receiving end grid, obtaining about two thirds of its demand from North China Power Grid. So an accurate prediction for the electricity load Beijing is very important for power dispatching and safety operation of the entire grid. However, the electricity load may be influenced by a combined effect of many complex factors, such as the industrial and agricultural productions, transportations, daily lives, weather and climate. The different factors may take different effects on the power load variability on various timescales. Major achievements are made through previous research, but it is still a challenge today to predict accurately the power load variability, especially in the daily time scales. A further and quantitative study on the daily power load variability and its main factors would be helpful for the precise prediction.Based on the daily electric power load and meteorological data of Beijing during the period from January 2006 to September 2010, an analysis is implemented with statistical method aiming for better understanding electric power load of Beijing and its main affecting factors in summer. The results indicate that temperature, wind speed and relative humidity are the major factors which are significantly correlated with the maximum electric power load in summer. Among these factors, the daily minimum temperature is the most influencing factor with a correlation coefficient of 0.65 and significance at 0.001 level. Considering the 1℃ effect for energy consumption, the daily maximum electric power load would increase 39.7×10⁷W with temperature rising 1℃ when the daily maximum temperature is higher than 26℃, or when the daily minimum temperature is higher than 18℃. Using the statistical regression model can roughly predict the maximum power load fluctuations. It can provide some reference for the power allocation decision in advance. Moreover, the effects of temperature humidity index (I_TH) on the variability of electric power load are also checked, where I_TH are expected to quantify the degree of human body comfort. The outcomes suggest that the I_TH can improve the explained variance of the daily maximum electric power load than a single temperature factor.