Multiple-day forecast of residential power consumption (PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE KYRGYZ REPUBLIC, 2026)
Load forecasting is a crucial step in achieving optimal power exchange and ensuring the robust operation of utilities, particularly in the active integration of distributed renewable energy sources (DRES). Deep learning methods have gained popularity among researchers due to their capability to generalize complex nonlinearity in power consumption fluctuations. Especially, recurrent neural networks are suitable for modeling electricity consumption as they have strong temporal patterns. This work aims to continue enhancing RNN models, which are built on base and manual features. The forecasting horizon has been extended to meet the requirements of practical application. Two approaches of multiple-day forecasting have been compared, and the proposed method outperformed the alternative recursive forecasting method.
Keywords: Load forecasting, deep learning methods, RNN model, manual feature, forecasting horizon, recursive forecasting, multiple-day forecasting