A Comparative Analysis of Machine Learning Methods for Short-Term Load Forecasting Systems

End-users' electricity consumption is highly affected by weather conditions. The uncertain nature of these circumstances can highly challenge energy supply and demand balancing. The identification of explanatory variables that influence energy usage plays a key role in addressing this issue. This paper conducts a benchmark study of several machine learning methods to compare their ability to determine the most significant weather-related variables and estimate energy demand. Accordingly, it investigates fifteen climate features as predictors. These components are entered into eight algorithms that select different sets of meaningful features. The selected characteristics are exploited by five other techniques to predict energy usage. Subsequently, the outcomes are evaluated to define the most efficient forecasting process. The results of the selection procedure demonstrate that mains water and dry outdoor temperatures are the most descriptive variables. With regard to both algorithmic steps, the random forest method provides the best results with 60.78% forecasting ability. Indeed, the remarks, elaborated by this study, can assist with designing the effective load forecasting structures.