کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Net Load Forecasting of Household Prosumers Considering Deep Reinforcement Learning

نویسندگان :

Behzad Motallebi Azar¹ Rasool Kazemzadeh² Morteza Zare Oskouei³ Behnam Mohammadi-Ivatloo⁴

1- Sahand University of Technology 2- Sahand University of Technology 3- Sahand University of Technology 4- Lappeenranta University of Technology

کلمات کلیدی :

Net load forecasting،Machine learning،Household prosumers،Deep reinforcement learning

چکیده :

The advent of machine learning (ML) has opened up a promising avenue for forecasting energy systems operations. Meanwhile, deep reinforcement learning (DRL) algorithms as advanced ML techniques attracted lots of attention. In this regard, this study employs the twin delayed deep deterministic policy gradient (TD3) DRL algorithm to forecast net load in a mid-term manner for heterogeneous household prosumers over consecutive days during summer and winter, highlighting seasonal variations. Moreover, all simulations are run based on real-world data to depict the efficiency of the proposed framework.

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