Multiple Linear Regression-Based Correlation Analysis of Various Critical Weather Factors and Solar Energy Generation in Smart Homes

The smart home concept, transforming traditional homes into smart homes thanks to technological advancements, is widespread around the world. In addition, energy consumers are also becoming energy producers by adding renewable energy sources, namely solar, wind, etc., to their homes along with traditional energy sources. However, intermittent weather conditions impact the power generation of renewable sources. Hence, there is a need to understand the correlation between several weather parameters and power generation. Traditional statistical methods such as Pearson, and Spearman, Kendall’s Tau, and Phi correlation coefficients are available but are limited to only two variables. Instead, multiple linear regression (MLR) offers multivariate analysis. Thus, this paper employs MLR to analyze the correlation between weather conditions such as temperature, apparent temperature, visibility, humidity, pressure, wind speed, dew point, precipitation, and power generation in kW. All the weather conditions are independent variables, and the generated power is a dependent variable. The key objective is to investigate the significant predictors and their impact on power generation. To implement this, a recent smart home dataset titled “Smart Home Dataset with Weather Information” that provides the required information was downloaded from Kaggle. This dataset contains 32 variables and 503,910 observations. The whole dataset with the considered variables (1 dependent variable and 11 independent variables) is utilized to implement the proposed correlation analysis. A regression model is developed to find the correlation between the parameters mentioned above in the dataset, and the multicollinearity among the independent variables is presented using the variance inflation factor (VIF). If the VIF value is more than 10, it represents high multicollinearity. The results showcase that those variables, such as temperature, humidity, apparent_temperature, and dew_point, produce VIF values of 296.67, 37.35, 126.29, and 152.15, respectively, and are thereby considered critical weather parameters that significantly influence solar energy generation. This aids in better generation and load management planning in smart homes.