6,161 research outputs found
Reinforcement Learning for the Unit Commitment Problem
In this work we solve the day-ahead unit commitment (UC) problem, by
formulating it as a Markov decision process (MDP) and finding a low-cost policy
for generation scheduling. We present two reinforcement learning algorithms,
and devise a third one. We compare our results to previous work that uses
simulated annealing (SA), and show a 27% improvement in operation costs, with
running time of 2.5 minutes (compared to 2.5 hours of existing
state-of-the-art).Comment: Accepted and presented in IEEE PES PowerTech, Eindhoven 2015, paper
ID 46273
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
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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
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
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