# Co-designing Intelligent Control of Building HVACs and Microgrids

arXiv: https://arxiv.org/abs/2107.08378 · HTML: https://mlsanity.com/paper/2107.08378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [One for Many: Transfer Learning for Building HVAC Control](https://mlsanity.com/paper/2008.03625/) (2020)
- [Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems](https://mlsanity.com/paper/1702.04699/) (2017)
- [Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle](https://mlsanity.com/paper/1804.00757/) (2018)

## Abstract (arXiv)

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. Therein, renewable resource-based microgrids offer a greener and cheaper alternative. This communication explores the possible co-design of microgrid power dispatch and building HVAC (heating, ventilation and air conditioning system) actuations with the objective of effective temperature control under minimised operating cost. For this, we attempt control designs with various levels of abstractions based on information available about microgrid and HVAC system models using the Deep Reinforcement Learning (DRL) technique. We provide control architectures that consider model information ranging from completely determined system models to systems with fully unknown parameter settings and illustrate the advantages of DRL for the design prescriptions.