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

TL;DR

This paper introduces a combined Seq2Seq and Q-learning approach for smart home energy management, improving load and PV power prediction accuracy and optimizing energy control under uncertainty.

Contribution

It presents a novel integration of Seq2Seq prediction with reinforcement learning for enhanced HEMS control, outperforming traditional methods.

Findings

Seq2Seq outperforms VARMA, SVR, and LSTM in prediction accuracy.

The integrated approach reduces energy costs in simulations.

Seq2Seq-based control improves online HEMS performance.

Abstract

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.