Home Energy Management System

Home Energy Management System: A Machine Learning Approach

Deivamani, Karthikeyan (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft DC systems, Energy conversion & Storage)

Shekhar, A. (mentor)
Bauer, P. (graduation committee)
Cremer, Jochen (graduation committee)

Delft University of Technology

Electrical Engineering | Sustainable Energy Technology

2023-10-10

The increasing adoption of renewable energy sources, particularly photovoltaic (PV) systems in residential sectors has raised important energy balancing challenges due to the intermittent nature of energy generation. To address these challenges and prioritize cost savings for residential consumers, this research investigates the integration of battery energy storage systems (BESS) and dynamic pricing strategies through an intelligent energy management system (EMS). Given the stochastic nature of PV generation, market prices, and load profile it is still challenging to achieve optimal control. Therefore Reinforcement Learning (RL)-based EMS is proposed in this research to make real-time optimal control decisions. RL is a machine learning approach where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. In this study, a deep deterministic policy gradient (DDPG) RL architecture is chosen due to its capability to handle continuous action spaces. In addition, deep learning-based models are employed to forecast uncontrollable load, PV generation and market prices for the integration into the EMS for which Bi-directional LSTM (Long Short Term Memory) was found to be the most accurate for all three uncertain variables. The DDPG algorithm is trained with data from a single household from the Lucerne region, Switzerland for 30 days and tested for a week. The results showed that compared to a deterministic rule-based approach the RL-based EMS increased cost savings for the end consumer by 14.2% but reduced the benefits for the grid operator to alleviate grid congestion quantified in terms of load factor, peak power consumption and ramping. Further work could be undertaken in testing the model on more extensive data and finding the best trade-off between customer and grid operator benefits.

Energy management system
Reinforcement Learning
forecasting
DDPG
Machine Learning

http://resolver.tudelft.nl/uuid:558378c8-fdca-4af9-97bc-b5f220a30e0d

Student theses

master thesis

© 2023 Karthikeyan Deivamani