The smart home culture is widely spread across the world by transforming traditional homes into smart homes with technological advancements. In addition, the consumers are becoming prosumers by adding renewable energy 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’s, 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, and precipitation, and the 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 gives the required information is downloaded from Kaggle. This dataset contains 32 columns and 503,910 observations. The whole dataset is considered for implementing the proposed correlation analysis. A regression model is developed to find the correlation between the above-mentioned parameters in the dataset, and the multicollinearity between the independent variables is presented using the variance inflation factor (VIF). If the VIF value is greater than 10, it represents high multicollinearity. The results showcase that the variables such as temperature, humidity, apparent temperature, and dew point have VIF values of 298.96, 37.54, 126.86, and 152.95, respectively, and are thereby considered critical weather parameters that significantly influence solar energy generation. This aids in better planning of generation and load management in smart homes.