Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning Article Swipe

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Mina Razghandi , Hao Zhou , Melike Erol‐Kantarci , Damla Turgut ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2109.12440

A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns. In this paper, we propose a sequence to sequence (Seq2Seq) learning-based supply and load prediction along with reinforcement learning-based HEMS control. We investigate how the prediction method affects the HEMS operation. First, we use Seq2Seq learning to predict photovoltaic (PV) power and home devices' load. We then apply Q-learning for offline optimization of HEMS based on the prediction results. Finally, we test the online performance of the trained Q-learning scheme with actual PV and load data. The Seq2Seq learning is compared with VARMA, SVR, and LSTM in both prediction and operation levels. The simulation results show that Seq2Seq performs better with a lower prediction error and online operation performance.

Related Topics

Computer Science

Reinforcement Learning

Photovoltaic System

Artificial Intelligence

Mathematical Analysis

Electrical Engineering

Mathematics

Statistics

Concepts

Computer science Reinforcement learning Sequence (biology) Energy management Photovoltaic system Artificial intelligence Scheme (mathematics) Energy (signal processing) Energy management system Machine learning Sequence learning Smart grid Real-time computing Engineering Biology Mathematical analysis Electrical engineering Genetics Mathematics Statistics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2109.12440
PDF: https://arxiv.org/pdf/2109.12440
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4286961796

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DOI: https://doi.org/10.48550/arxiv.2109.12440

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Title: Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-09-25

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Authors: Mina Razghandi, Hao Zhou, Melike Erol‐Kantarci, Damla Turgut

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Landing page: https://arxiv.org/abs/2109.12440

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PDF URL: https://arxiv.org/pdf/2109.12440

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Concepts: Computer science, Reinforcement learning, Sequence (biology), Energy management, Photovoltaic system, Artificial intelligence, Scheme (mathematics), Energy (signal processing), Energy management system, Machine learning, Sequence learning, Smart grid, Real-time computing, Engineering, Biology, Mathematical analysis, Electrical engineering, Genetics, Mathematics, Statistics

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