Learning Occupancy Prediction Models with Decision-Guidance Query Language

Alrazgan, Abdullah; Nagarajan, A.; Brodsky, Alexander; Egge, Nathan E.

doi:10.1109/hicss.2011.281

Cited by 6 publications

(3 citation statements)

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“…Disadvantages common for all presented solutions are temporary absences that could lead to errors in predictions and computing complexity. Authors of the paper [4] present the rule based system where rules are being generated automatically and optimized by mathematical programming algorithms. The authors in [7] presented a concept of the self-programming thermostat that creates an optimal setback schedule on the basis of the occupancy statistics of the respective home.…”

Section: B Related Workmentioning

confidence: 99%

Exploring possibilities of predictive self-programming thermostats for energy savings

et al. 2012

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Section: B Related Workmentioning

confidence: 99%

Exploring possibilities of predictive self-programming thermostats for energy savings

et al. 2012

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“…Reaching carbon neutrality goals require that our use of office building real-estate become more energy efficient. Different studies (Rahaman, 2019;Alrazgan, 2011;LeMay, 2009) explore how to optimize the use of space using digital tools both so occupants require less space and to only provide comfort in used spaces to more efficiently spend energy. Rahamen (2019) studies how to sense the used spaces of people to optimize for this and Kahn (2020) explore how to optimize the personal comfort of the space used by occupants.…”

Section: Introductionmentioning

confidence: 99%

Contextualizing the Accuracy-Fairness Tradeoff in Algorithmic Prediction Outcomes

Arhin,

Treku

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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In this paper, a probabilistic machine learning method is proposed to predict the indoor temperature of an office environment. An IOHMM-based model is developed to represent the office environment under different circumstances of heating sources. One year of time series data is observed and studied to learn the dynamics of the indoor thermal states. The uncertainty associated with the changing aspects of the indoor temperature and its dependence on the outdoor temperature is considered in the model development. The well-known Baum Welch and forward-backward algorithms are adapted to learn the model parameters. Then, the Viterbi algorithm is used to predict the maximum path of hidden states, leading to predicting the most likely future temperatures. A numerical application is presented to demonstrate the model development steps and the training and testing results. Finally, the model's performance is validated using leave-one-out cross-validation, which shows that the model has a prediction accuracy of about 78%.

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“…The DGQL Occupancy Prediction Model (DOPM) is an occupancy prediction model built using the Decision Guidance Query Language (DGQL) [155]. Its aims are to maximize energy saved in a location while limiting the inconvenience caused to its occupants.…”

Section: Background and Related Workmentioning

confidence: 99%

Application Development for Cyber-Physical Systems: Programming Language Concepts and Case Studies

Sookoor

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Cyber-Physical Systems (CPSs) combine low-power radios with tiny embedded processors in order to simultaneously cover large geographic areas and provide high-resolution sensing/actuation. However, CPSs are extremely difficult to program. This issue is addressed by presenting a macroprogramming framework called MacroLab that offers a vector programming abstraction similar to Matlab for Cyber-Physical Systems. The user writes a single program for the entire network using Matlab-like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the twowhichever is most efficient for the target deployment. This approach is called deployment-specific code decomposition (DSCD). MacroLab programs can be executed on mote-class hardware such as the Telos motes. The results indicate that MacroLab introduces almost no additional overhead in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS programs.As a crucial component of the application development cycle, debugging CPSs is addressed by MDB, the first system to support the debugging of macroprograms. MDB allows the user to set breakpoints and step through a macroprogram using a source-level debugging interface similar to GDB, a process called macrodebugging. A key challenge of MDB is to step through a macroprogram in sequential order even though it executes on the network in a distributed, asynchronous manner.Besides allowing the user to view distributed state, MDB also provides the ability to search for bugs over the entire history of distributed states. Finally, MDB allows the user to make hypothetical changes to a macroprogram and to see the effect on distributed state without the need to redeploy, execute, and test the new code. Macrodebugging is both easy and efficient: MDB consumes few system resources and requires few user commands to find the cause of bugs. A lightweight version of MDB, called MDB Lite, is also provided. It can be used during the deployment phase to reduce resource consumption while still eliminating the possibility of heisenbugs: changes in the manifestation i ii of bugs caused by enabling or disabling the debugger.While a large number of CPS applications have been developed over the last few years, very few, if any, have been developed using a macroprogramming language. This is in spite of the vast array of macroprogramming languages, and abstractions, that are available. In an attempt to understand why macroprogramming languages are not being used for CPS application development and in order to evaluate the effectiveness of MacroLab in implementing a real-world CPS, a series of case studies involving the occupancy-based control of a Heating, Ventilation, and Air Conditioning (HVAC) system is implemented. Occupant-oriented HVAC control was selected due to it being a complete cyber-physical system involving the interaction between the real world and computation. Unlike many sensor network applications ...

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Learning Occupancy Prediction Models with Decision-Guidance Query Language

Cited by 6 publications

References 10 publications

Exploring possibilities of predictive self-programming thermostats for energy savings

Exploring possibilities of predictive self-programming thermostats for energy savings

Contextualizing the Accuracy-Fairness Tradeoff in Algorithmic Prediction Outcomes

Application Development for Cyber-Physical Systems: Programming Language Concepts and Case Studies

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