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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Scaling reinforcement learning to the unconstrained multi-agent domain
Reinforcement learning is a machine learning technique designed to mimic the
way animals learn by receiving rewards and punishment. It is designed to train
intelligent agents when very little is known about the agent’s environment, and consequently
the agent’s designer is unable to hand-craft an appropriate policy. Using
reinforcement learning, the agent’s designer can merely give reward to the agent when
it does something right, and the algorithm will craft an appropriate policy automatically.
In many situations it is desirable to use this technique to train systems of agents
(for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately,
several significant computational issues occur when using this technique
to train systems of agents. This dissertation introduces a suite of techniques that
overcome many of these difficulties in various common situations.
First, we show how multi-agent reinforcement learning can be made more tractable
by forming coalitions out of the agents, and training each coalition separately. Coalitions
are formed by using information-theoretic techniques, and we find that by using
a coalition-based approach, the computational complexity of reinforcement-learning
can be made linear in the total system agent count. Next we look at ways to integrate
domain knowledge into the reinforcement learning process, and how this can signifi-cantly improve the policy quality in multi-agent situations. Specifically, we find that
integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions
when lack of training data would have prevented such convergence without domain
knowledge. We then show how to train policies over continuous action spaces, which
can reduce problem complexity for domains that require continuous action spaces
(analog controllers) by eliminating the need to finely discretize the action space. Finally,
we look at ways to perform reinforcement learning on modern GPUs and show
how by doing this we can tackle significantly larger problems. We find that by offloading
some of the RL computation to the GPU, we can achieve almost a 4.5 speedup
factor in the total training process
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