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سی و سومین کنفرانس بین المللی مهندسی برق
Net Load Forecasting of Household Prosumers Considering Deep Reinforcement Learning
نویسندگان :
Behzad Motallebi Azar
1
Rasool Kazemzadeh
2
Morteza Zare Oskouei
3
Behnam Mohammadi-Ivatloo
4
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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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
