Predictive Analysis of Monthly Electricity Consumption Using Rule-Based and Machine Learning Models

Accurate monthly electricity consumption (EC) forecasting is essential for power providers to allocate resources efficiently, develop reasonable sales plans, and support the creation of reliable smart grids and precise demand-side management policies. Given that factors such as climate, population, and economic conditions can significantly impact EC, it is crucial to consider a wide range of variables in medium-term EC forecasts. This paper addresses a gap in the existing literature by evaluating the performance of the M5 rule model—a relatively underutilized technique—in comparison with popular machine learning (ML) models like Random Forest (RF) and Support Vector Machine (SVM). The motivation for selecting the M5 rule regression technique stems from its effective feature selection process, which is simpler and more straightforward than the complex feature selection methods employed by other models. Using an aggregated dataset from the Czech Transmission System Operator, the study applies these three regression techniques independently to forecast monthly EC. The results demonstrate that the M5 rule regression model outperforms both SVM and RF models for monthly forecasts, achieving an impressive correlation coefficient (R²) value of 0.9063, compared to 0,8915 for SVM and 0,8598 for RF. Additionally, the M5 rule achieves the lowest Mean Absolute Error (MAE) of 16772.29, compared to 17477.57 for SVM and 21390.68 for RF, as well as the lowest Root Mean Squared Error (RMSE) of 22287.94, compared to 23114.17 for SVM and 26658.89 for RF. Furthermore, M5 rule shows superior performance in terms of relative errors, with a Relative Absolute Error (RAE) of 43.12% and a Relative Root Mean Squared Error (RRSE) of 45.74%, while RF and SVM show higher values. The M5 rule model also identifies air temperature, relative humidity, and clear sky surface irradiance as the most influential features in predicting EC. These findings offer valuable implications for power management companies, aiding in the strategic planning of power generation and supply. By accurately forecasting EC and understanding key influencing factors, companies can better avoid issues of overproduction or shortages, leading to more efficient and reliable power management.