The current paper is a full statistical study of solar power generation and capacity utilisation using a huge sample (161,864 half-hourly observations) of the photovoltaic grid supply infrastructure in the United Kingdom. The primary goal is to analyse the dynamics in the production of solar energy and compare the effectiveness of using capacity under seasonal and diurnal variations. The information, acquired with the help of the Kaggle open-data platform, represents the data about the actual generation in megawatts (MW), the actual capacity, lower and upper confidence limits of the generation forecast, and the inferred data, such as the percentage of the capacity utilisation, and the range of the prediction interval. The statistical tests on descriptive statistics, Pearson correlation analysis, one-way analysis of variance (ANOVA) with a post hoc Tukey HSD, and multiple linear regression were done using IBM SPSS Statistics. The results show that the mean solar output varies significantly across the seasons, with the highest mean (M = 1959.49 MW, SD = 2362.23) and lowest (M = 492.23 MW, SD = 1057.79) in summer and winter, respectively. Diurnal analysis indicated the afternoon hours showed the highest generation (M = 2866.20 MW), and low generation in the night (M = 11.17 MW). The overall capacity utilisation was just 10.04, and this implies that the installed photovoltaic infrastructure was not well used. The outcomes present some practical information that may be utilised by grid operators, renewable energy planners and energy policymakers to utilise solar energy optimally in the national power systems.