1,376 research outputs found
Designing a generalised reward for Building Energy Management Reinforcement Learning agents
The reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.The work described in this paper was partially supported by the Basque Government under ELKARTEK project (LANTEGI4.0 KK-2020/00072)
B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Batch reinforcement learning (BRL) is an emerging research area in the RL
community. It learns exclusively from static datasets (i.e. replay buffers)
without interaction with the environment. In the offline settings, existing
replay experiences are used as prior knowledge for BRL models to find the
optimal policy. Thus, generating replay buffers is crucial for BRL model
benchmark. In our B2RL (Building Batch RL) dataset, we collected real-world
data from our building management systems, as well as buffers generated by
several behavioral policies in simulation environments. We believe it could
help building experts on BRL research. To the best of our knowledge, we are the
first to open-source building datasets for the purpose of BRL learning
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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
BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Recent advancements in reinforcement learning algorithms have opened doors
for researchers to operate and optimize building energy management systems
autonomously. However, the lack of an easily configurable building dynamical
model and energy management task simulation and evaluation platform has
arguably slowed the progress in developing advanced and dedicated reinforcement
learning (RL) and control algorithms for building operation tasks. Here we
propose "BEAR", a physics-principled Building Environment for Control And
Reinforcement Learning. The platform allows researchers to benchmark both
model-based and model-free controllers using a broad collection of standard
building models in Python without co-simulation using external building
simulators. In this paper, we discuss the design of this platform and compare
it with other existing building simulation frameworks. We demonstrate the
compatibility and performance of BEAR with different controllers, including
both model predictive control (MPC) and several state-of-the-art RL methods
with two case studies.Comment: Accepted at ACM e-Energy 2023; Code available at
https://github.com/chz056/BEA
An investigation into the energy and control implications of adaptive comfort in a modern office building
PhD ThesisAn investigation into the potentials of adaptive comfort in an office
building is carried out using fine grained primary data and computer
modelling. A comprehensive literature review and background study into
energy and comfort aspects of building management provides the
backdrop against which a target building is subjected to energy and
comfort audit, virtual simulation and impact assessment of adaptive
comfort standard (BS EN 15251: 2007). Building fabric design is also
brought into focus by examining 2006 and 2010 Approved Document
part L potentials against Passive House design. This is to reflect the
general direction of regulatory development which tends toward zero
carbon design by the end of this decade. In finishing a study of modern
controls in buildings is carried out to assess the strongest contenders that
next generation heating, ventilation and air-conditioning technologies
will come to rely on in future buildings.
An actual target building constitutes the vehicle for the work described
above. A virtual model of this building was calibrated against an
extensive set of actual data using version control method. The results
were improved to surpass ASHRAE Guide 14. A set of different scenarios
were constructed to account for improved fabric design as well as
historical weather files and future weather predictions. These scenarios
enabled a comparative study to investigate the effect of BS EN
15251:2007 when compared to conventional space controls.
The main finding is that modern commercial buildings built to the latest
UK statutory regulations can achieve considerable carbon savings
through adaptive comfort standard. However these savings are only
modestly improved if fabric design is enhanced to passive house levels.
Adaptive comfort can also be readily deployed using current web-enabled
control applications. However an actual field study is necessary to
provide invaluable insight into occupants’ acceptance of this standard
since winter-time space temperature results derived from BS EN
15251:2007 constitute a notable departure from CIBSE environmental
guidelines
Neural network based predictive control of personalized heating systems
The aim of a personalized heating system is to provide a desirable microclimate for each individual when heating is needed. In this paper, we present a method based on machine learning algorithms for generation of predictive models for use in control of personalized heating systems. Data was collected from two individual test subjects in an experiment that consisted of 14 sessions per test subject with each session lasting 4 h. A dynamic recurrent nonlinear autoregressive neural network with exogenous inputs (NARX) was used for developing the models for the prediction of personalized heating settings. The models for subjects A and B were tested with the data that was not used in creating the neural network (unseen data) to evaluate the accuracy of the prediction. Trained NARX showed good performance when tested with the unseen data, with no sign of overfitting. For model A, the optimal network was with 12 hidden neurons with root mean square error equal to 0.043 and Pearson correlation coefficient equal to 0.994. The best result for model B was obtained with a neural network with 16 hidden neurons with root mean square error equal to 0.049 and Pearson correlation coefficient equal to 0.966. In addition to the neural network models, several other machine learning algorithms were tested. Furthermore, the models were on-line tested and the results showed that the test subjects were satisfied with the heating settings that were automatically controlled using the models. Tests with automatic control showed that both test subjects felt comfortable throughout the tests and test subjects expressed their satisfaction with the automatic control
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