17 research outputs found
A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks
Energy game-theoretic frameworks have emerged to be a successful strategy to
encourage energy efficient behavior in large scale by leveraging
human-in-the-loop strategy. A number of such frameworks have been introduced
over the years which formulate the energy saving process as a competitive game
with appropriate incentives for energy efficient players. However, prior works
involve an incentive design mechanism which is dependent on knowledge of
utility functions for all the players in the game, which is hard to compute
especially when the number of players is high, common in energy game-theoretic
frameworks. Our research proposes that the utilities of players in such a
framework can be grouped together to a relatively small number of clusters, and
the clusters can then be targeted with tailored incentives. The key to above
segmentation analysis is to learn the features leading to human decision making
towards energy usage in competitive environments. We propose a novel graphical
lasso based approach to perform such segmentation, by studying the feature
correlations in a real-world energy social game dataset. To further improve the
explainability of the model, we perform causality study using grangers
causality. Proposed segmentation analysis results in characteristic clusters
demonstrating different energy usage behaviors. We also present avenues to
implement intelligent incentive design using proposed segmentation method.Comment: Proceedings of the Special Session on Machine Learning in Energy
Application, International Conference on Machine Learning and Applications
(ICMLA) 2019. arXiv admin note: text overlap with arXiv:1810.1053
Machine Learning for Smart and Energy-Efficient Buildings
Energy consumption in buildings, both residential and commercial, accounts
for approximately 40% of all energy usage in the U.S., and similar numbers are
being reported from countries around the world. This significant amount of
energy is used to maintain a comfortable, secure, and productive environment
for the occupants. So, it is crucial that the energy consumption in buildings
must be optimized, all the while maintaining satisfactory levels of occupant
comfort, health, and safety. Recently, Machine Learning has been proven to be
an invaluable tool in deriving important insights from data and optimizing
various systems. In this work, we review the ways in which machine learning has
been leveraged to make buildings smart and energy-efficient. For the
convenience of readers, we provide a brief introduction of several machine
learning paradigms and the components and functioning of each smart building
system we cover. Finally, we discuss challenges faced while implementing
machine learning algorithms in smart buildings and provide future avenues for
research at the intersection of smart buildings and machine learning
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Data-Centric Machine Learning for Human-Centric Applications
Climate change and pandemics are two of the most pressing threats facing humanity today. Addressing these urgent threats require immediate mitigative actions. In the US, buildings are responsible for 40% of primary energy consumption, 73% of electrical use and 40% of greenhouse gas emissions, the primary cause of global warming, and such high levels are now rapidly spreading across the rest of the world. At the same time, buildings are integral to human lives, as we spend most of our time in them which substantially affects our health and productivity. So, for climate change mitigation, it is essential to optimize energy use in buildings while ensuring human comfort. On the other hand, for pandemics mitigation, it is crucial to diagnose and have a better understanding of the new disease in a time-sensitive manner. Over the years, Machine Learning (ML) as a tool has been widely utilized for both the above efforts. However, both buildings and pandemic-specific healthcare systems exhibit a number of shared data-specific challenges, hindering robust ML implementations.We will present 3 major research works on tackling them with generative modeling, and transfer learning. The first work will be on conditional synthetic data generation, where the focus is to conditionally generate synthetic data for classes with infrequent data points. The applications include tackling class imbalance in healthcare data, and privacy-preserving data sharing. The second will be on improved pre-processing methods for tabular data (a common data type in smart buildings) to enable seamless use by many ML algorithms. To improve the generalizability and scalability of the models, the third work will be on a transfer learning-based adversarial domain adaptation method, with applications in adapting personal thermal comfort models in buildings from one occupant to another without using any data labels for the target occupant. With this method, the time and the resource-intensive task of acquiring multiple labels for the target environment in a building can be avoided
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Likelihood Contribution based Multi-scale Architecture for Generative Flows
Deep generative modeling using flows has gained popularity owing to the
tractable exact log-likelihood estimation with efficient training and synthesis
process. However, flow models suffer from the challenge of having high
dimensional latent space, the same in dimension as the input space. An
effective solution to the above challenge as proposed by Dinh et al. (2016) is
a multi-scale architecture, which is based on iterative early factorization of
a part of the total dimensions at regular intervals. Prior works on generative
flow models involving a multi-scale architecture perform the dimension
factorization based on static masking. We propose a novel multi-scale
architecture that performs data-dependent factorization to decide which
dimensions should pass through more flow layers. To facilitate the same, we
introduce a heuristic based on the contribution of each dimension to the total
log-likelihood which encodes the importance of the dimensions. Our proposed
heuristic is readily obtained as part of the flow training process, enabling
the versatile implementation of our likelihood contribution based multi-scale
architecture for generic flow models. We present such implementations for
several state-of-the-art flow models and demonstrate improvements in
log-likelihood score and sampling quality on standard image benchmarks. We also
conduct ablation studies to compare the proposed method with other options for
dimension factorization
Focus on what matters: improved feature selection techniques for personal thermal comfort modelling
Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods