Statistical weather data analysis for wide area smart grid operations

In deregulated power market, the variation of demand curves with the varying weather parameters is a probabilistic problem. The weather parameters, such as temperature, humidity, and precipitation affect the load requirements in non-autonomous wide area smart grid system. We modeled the relationships of the load with aforementioned weather parameters. Moreover, various correlation models, such as Pearson, Spearman, and Kendall are quantitatively analyzed for each independent weather data variables. Furthermore, simple linear regression and multi-linear regression techniques are applied for the statistical varying load model in the wide area smart grid system. The effect of each independent variable on the dependent variable (load) is critically observed using statistical tool R. The linear and non-linear varying patterns exhibited monitoring information for the load control and management of wide area smart grid system.