Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning
This work addresses energy cost reduction for smart home customers by improving prediction and control, but it is incremental as it builds on existing sequence-to-sequence and reinforcement learning techniques.
The paper tackles uncertainty in smart home energy management by proposing a sequence-to-sequence learning model for predicting photovoltaic power and device loads, combined with Q-learning for control, achieving lower prediction errors and better online operation performance compared to VARMA, SVR, and LSTM methods.
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.