Efficient Deep Reinforcement Learning for Smart Buildings: Integrating Energy Storage Systems Through Advanced Energy Management Strategies

This study presents a novel and workable approach to solving the critical issue of improving energy management in smart buildings. Using a large dataset from a seven-story office building in Bangkok, Thailand, our work introduces a novel approach that combines Deep Q-network (DQN) algorithms with energy storage models and cost optimization strategies. The suggested approach is intended to reduce operational expenses, improve the energy economic performance, and efficiently control peak demand. The energy storage model used in this research incorporates the use of the capabilities of advanced storage models in smart buildings, particularly lithium-ion batteries and supercapacitors. When the cost optimization approach is applied using linear programming, energy consumption costs are significantly reduced. Notably, our method outperforms current algorithms, specifically outperforming them, to show its effectiveness in smart building energy management by outperforming current algorithms, especially Genetic and Fuzzy Algorithms. In comparison to traditional methods, the DQN algorithm exhibits an impressive 8.6% reduction in Mean Square Error (MSE) and a 6.4% drop in Mean Absolute Error (MAE), making it a standout performer in the research through Python software. The results highlight the significance of optimizing DQN algorithm parameters for best outcomes, with a focus on adaptability to various properties of smart buildings. This investigation is novel because it integrates cost optimization, reinforcement learning, and energy storage. This results in a flexible and all-inclusive framework that can be used for effective and sustainable energy management in smart buildings.

@article{Farhana2023,
  title     = {Efficient Deep Reinforcement Learning for Smart Buildings: Integrating Energy Storage Systems Through Advanced Energy Management Strategies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Artika Farhana and Nimmati Satheesh and Ramya M and Janjhyam Venkata Naga Ramesh and Yousef A. Baker El-Ebiary},
  doi       = {10.14569/IJACSA.2023.0141257},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141257}
}