2,725 research outputs found
A Deep Reinforcement Learning Method for Model-based Optimal Control of HVAC Systems
Model-based optimal control (MOC) methods have strong potential to improve the energy efficiency of heating, ventilation and air conditioning (HVAC) system. However, most existing MOC methods require a low-order building model, which significantly limits the practicability of such methods. This study develops a novel model-based optimal control method for HVAC supervisory-level control based on the recently-proposed deep reinforcement learning (DRL) framework. The control method can directly use whole building energy model, a widely used flexible building modelling method, as the model and train an optimal control policy using DRL. By integrating deep learning models, the proposed control method can directly take the easily-measurable parameters, such as weather conditions and indoor environment conditions, as the input and controls the easily-controllable supervisory-level control points of HVAC systems. The proposed method is tested in an office building to control its radiant heating system. It is found that a dynamic optimal control policy can be successfully developed, and better heating energy efficiency can be achieved while maintaining the acceptable indoor thermal comfort. However, the “delayed reward problem” is found, which indicates the future work should firstly focus on the effective optimization of the deep reinforcement learning
Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks
Modern buildings encompass complex dynamics of multiple electrical,
mechanical, and control systems. One of the biggest hurdles in applying
conventional model-based optimization and control methods to building energy
management is the huge cost and effort of capturing diverse and temporally
correlated dynamics. Here we propose an alternative approach which is
model-free and data-driven. By utilizing high volume of data coming from
advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could
accurately represent the operation's temporal dynamics of building complexes.
The trained network is then directly fitted into a constrained optimization
problem with finite horizons. By reformulating the constrained optimization as
an unconstrained optimization problem, we use iterative gradient descents
method with momentum to find optimal control inputs. Simulation results
demonstrate proposed method's improved performances over model-based approach
on both building system modeling and control
Recommended from our members
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
Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning
The conventional control paradigm for a heat pump with a less efficient
auxiliary heating element is to keep its temperature set point constant during
the day. This constant temperature set point ensures that the heat pump
operates in its more efficient heat-pump mode and minimizes the risk of
activating the less efficient auxiliary heating element. As an alternative to a
constant set-point strategy, this paper proposes a learning agent for a
thermostat with a set-back strategy. This set-back strategy relaxes the
set-point temperature during convenient moments, e.g. when the occupants are
not at home. Finding an optimal set-back strategy requires solving a sequential
decision-making process under uncertainty, which presents two challenges. A
first challenge is that for most residential buildings a description of the
thermal characteristics of the building is unavailable and challenging to
obtain. A second challenge is that the relevant information on the state, i.e.
the building envelope, cannot be measured by the learning agent. In order to
overcome these two challenges, our paper proposes an auto-encoder coupled with
a batch reinforcement learning technique. The proposed approach is validated
for two building types with different thermal characteristics for heating in
the winter and cooling in the summer. The simulation results indicate that the
proposed learning agent can reduce the energy consumption by 4-9% during 100
winter days and by 9-11% during 80 summer days compared to the conventional
constant set-point strategyComment: Submitted to Energies - MDPI.co
- …