Analysis of factors influencing electricity consumption of university's dormitories during Post-COVID-19 recovery

Abstract

In recent times, there has been a significant increase in global and Thai electricity consumption. This surge has led people to seek ways to save on electricity costs, such as installing solar panels. However, it appears to be a solution addressing the symptom rather than the root cause, as people continue to consume electricity at similar levels. Understanding the factors influencing electricity usage is crucial for tackling the root cause, as it enables the reduction of activities or behaviors leading to excessive energy consumption. This research aims to investigate the factors influencing electricity consumption in university dormitories, specifically focusing on the electricity bills. Data was collected through a total of 243 surveys, rigorously verified and prepared for analysis. The survey yielded a total of 35 factors, which were then analyzed to identify their relationships with electricity consumption. The 16 factors were found to correlate with electricity usage based on Spearman's correlation, while 19 factors were identified through MI. To simplify the data and reduce complexity, EFA was employed, resulting in only 7 common factors from both Spearman's correlation and MI analyses. Each dataset was utilized to build predictive models for electricity consumption using five algorithms: SVM, MLP, KNN, DT, and LR. The baseline model, performing best in terms of learning efficiency with the dataset analyzed for correlation with electricity consumption using MI's 19 factors and SVM, achieved a testing accuracy score of 0.5762. To enhance the processing efficiency of the baseline model, parameter tunings were made for the SVM, with C set to 1.5, gamma set to 4.699, and using the “rbf” kernel. Post-training and evaluation, the adjusted model exhibited a testing accuracy score of 0.7353, indicating that parameter tuning positively affected the predictive performance of the model for real-world scenarios. From the information gathered, it can be concluded that factors influencing electricity consumption include the number of notebooks operating on the Windows operating system, the duration of computer usage for both learning and gaming, activities such as ironing or using a hair dryer combined with turning on the air conditioner for heat dissipation, knowledge about electricity usage (e.g., choosing electrical appliances labeled with the number 5), and finally, attitudes towards electricity usage.