Multi-Output Machine Learning for Short-Term Electricity Load Forecasting Using Cooling and Heating Degree Days in Tirana, Albania

Abstract

Accurate short-term forecasting of electricity loads is crucial for power system operations in regions with large fluctuations in daily energy demand caused by climate variability. This study presents a next-day electricity load forecasting for Tirana, Albania, by combining temperature-based indicators, Cooling Degree Days and Heating Degree Days in a multi- input-output modeling framework. The forecasting of daily electricity load and degree day variables was carried out using several forecasting methods, including Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting and Artificial Neural Networks, over training and testing scenarios based on the daily data collected from 2020 to 2022. The forecasting framework includes short-term temporal and temperature-dependent dynamics using lagged electricity demand and degree-day indicators. The model performance was evaluated under three different seasonal scenarios: August (high cooling demand), October (neutral transition conditions), and November (high heating demand). The results indicate that under neutral conditions, all models exhibit similar performance, with an average MAPE of approximately 2.7% in October. Random Forest has the best load forecasting accuracy in August (MAPE= 4.07%), and Support Vector Regression has the best load forecasting accuracy in November (MAPE= 3.27%), followed by the tree- based ensemble methods. The results show that forecasting performance is season-dependent, with different models achieving the highest accuracy under different climatic conditions. Rather than identifying a single globally optimal model, the findings highlight the importance of regime-specific model behavior in electricity demand forecasting. This suggests that model selection should be aligned with seasonal characteristics of the load, particularly in temperature-sensitive systems. The findings provide useful information for short-term operational planning in the Albanian electricity sector.