# NeurOpt: Neural network based optimization for building energy management and climate control

arXiv: https://arxiv.org/abs/2001.07831 · HTML: https://mlsanity.com/paper/2001.07831/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Low Emission Building Control with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2208.06385/) (2022)
- [Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques](https://mlsanity.com/paper/2104.02214/) (2021)
- [Active Distribution System Coordinated Control Method via Artificial Intelligence](https://mlsanity.com/paper/2207.14642/) (2022)
- [Low Emission Building Control with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2206.14191/) (2022)
- [Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models](https://mlsanity.com/paper/2609.12853/) (2026)

## Abstract (arXiv)

Model predictive control (MPC) can provide significant energy cost savings in building operations in the form of energy-efficient control with better occupant comfort, lower peak demand charges, and risk-free participation in demand response. However, the engineering effort required to obtain physics-based models of buildings is considered to be the biggest bottleneck in making MPC scalable to real buildings. In this paper, we propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. Our approach does not require building domain expertise or retrofitting of existing heating and cooling systems. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy. We learn dynamical models of energy consumption and zone temperatures with high accuracy and demonstrate energy savings and better occupant comfort compared to the default system controller.