Tianshu Wei
ABSTRACT
Smart buildings in the future are complex cyber-physical-human systems that involve close interactions among embedded platform (for sensing, computation, communication and control), mechanical components, physical environment, building architecture, and occupant activities. The design and operation of such buildings require a new set of methodologies and tools that can address these heterogeneous domains in a holistic, quantitative and automated fashion. In this paper, we will present our design automation methods for improving building energy efficiency and offering comfortable services to occupants at low cost. In particular, we will highlight our work in developing both model-based and data-driven approaches for building automation and control, including methods for co-scheduling heterogeneous energy demands and supplies, for integrating intelligent building energy management with grid optimization through a proactive demand response framework, for optimizing HVAC control with deep reinforcement learning, and for accurately measuring in-building temperature by combining prior modeling information with few sensor measurements based upon Bayesian inference.
- [1].. 2012. Identifying Models of HVAC Systems Using Semiparametric Regression. In Proc. American Control Conference. 3675–3680.Google Scholar
- [2].. 2015. Autonomous HVAC Control, A Reinforcement Learning Approach. Springer.Google Scholar
- [3].. 1976. The equivalence of generalized least squares and maximum likelihood estimates in the exponential family. J. Amer. Statist. Assoc. 71, 353 (1976), 169–171.Google ScholarCross Ref
- [4].. 2015. Experimental analysis of data-driven control for a building heating system. CoRR abs/1507.03638 (2015).Google Scholar
- [5].DOE. 2006. Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them. (2006).Google Scholar
- [6].DOE. 2010. Building Energy Data Book of DOE. website: http://buildingsdatabook.eren.doe.govGoogle Scholar
- [7].. 2005 Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region. Energy and Buildings 37 (2005), 545–553.Google ScholarCross Ref
- [8].Energy efficiency trends in residential and commercial buildings. 2010. http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/building_trends_2010.pdfGoogle Scholar
- [9].EnergyPlus. 1996. https://energyplus.net/Google Scholar
- [10]. 2014. Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems. Journal of Ambient Intelligence and Smart Environments (2014), 675–690.Google Scholar
- [11]. 2005. The elements of statistical learning: data mining, inference and prediction (2 ed.). Springer.Google Scholar
- [12]. 2012 Gaussian Process Modeling for Measurement and Verification of Building Energy Savings. Energy and Buildings 53 (2012), 7–18.Google ScholarCross Ref
- [13].. 2012. Lecture 6a Overview of mini-batch gradient descent. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdfGoogle Scholar
- [14]. 2012. Multivariate regression as an energy assessment tool in early building design. Building and Environment 57, (2012), 165–175.Google ScholarCross Ref
- [15].. 2015. A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings. IEEE International Conference on Automation Science and Engineering (CASE) (2015), 444–449.Google Scholar
- [16].. 2011. Optimal demand response based on utility maximization in power networks. IEEE Power and Energy Society General Meeting (2011).Google Scholar
- [17].. 2010. Development and Applications of Hourly Building Cooling Load Prediction Model. In IEEE International Conference on Advances in Energy Engineering.Google Scholar
- [18]. 2013. Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits. In Computer-Aided Design (ICCAD), 2013 IEEE/ACM International Conference on. 795–802. https://doi.org/10.1109/ICCAD.2013.6691204Google Scholar
- [19]. 2012. Efficient parametric yield estimation of analog/mixed-signal circuits via Bayesian model fusion. In Computer-Aided Design (ICCAD), 2012 IEEE/ACM International Conference on. 627–634.Google Scholar
- [20].. 2012. Model predictive control for the operation of building cooling systems. IEEE Transactions on Control Systems Technology 20, 3 (2012), 796–803.Google ScholarCross Ref
- [21].. 2011. Model-based hierarchical optimal control design for HVAC systems. ASME Dynamic System Control Conference (DSCC) (2011).Google Scholar
- [22]. 1992. Latin hypercube sampling as a tool in uncertainty analysis of computer models. In Proceedings of the 24th Conference on Winter Simulation. 557–564.Google Scholar
- [23]. 2015. Human-level control through deep reinforcement learning. Nature 518 (Feb 2015), 529–533.Google ScholarCross Ref
- [24].. 2008. Comparison Between Detailed Model Simulation and Artificial Neural Network for Forecasting Building Energy Consumption. Energy and Buildings 40 (2008), 2169–2176.Google ScholarCross Ref
- [25].. 2013. A method for computing optimal set-point schedules for HVAC systems. (2013).Google Scholar
- [26].. 2010. Energy efficient building climate control using stochastic model predictive control and weather predictions. American Control Conference (ACC) (2010), 5100–5105.Google Scholar
- [27]. 2014. Development of a Probabilistic Graphical Energy Performance Model for an Office Building. In Proc. ASHRAE Annual Meeting.Google Scholar
- [28]. 2005. Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method. Springer.Google ScholarDigital Library
- [29]. 2014. Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation. Energy and Buildings 76, 0 (2014), 381–391.Google Scholar
- [30].Market Research Store. 2015. Global Smart Building Market Set for Rapid Growth, To Reach Around USD 36.0 Billion by 2020. Accessed online May 2018 from http://www.marketresearchstore.com/news/global-smart-building-market-set-for-rapid-growth-105Google Scholar
- [31].. 2014. Online Coordinated Charging Decision Algorithm for Electric Vehicles Without Future Information. IEEE Transactions on Smart Grid 5, 4 (Nov 2014), 2810–2824.Google Scholar
- [32].. 2011. A Dynamic Machine Learning-based Technique for Automated Fault Detection in HVAC Systems. ASHRAE Transactions 117 (2011), 449–456.Google Scholar
- [33].. 2013. Bayesian Model Fusion: Large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data. In Design Automation Conference (DAC), 2013 50th ACM/EDAC/IEEE. 1–6.Google Scholar
- [34].. 1992. Q-learning. Machine learning 8, 3–4 (1992), 279–292.Google ScholarDigital Library
- [35].. 2014. Battery Management and Application for Energy-Efficient Buildings. Design Automation Conference on Design Automation Conference (DAC) (2014).Google Scholar
- [36].. 2017. Deep reinforcement learning for building HVAC control. ACM/EDAC/IEEE Design Automation Conference (DAC), 1–6.Google Scholar
- [37].. 2014. Co-scheduling of HVAC Control, EV Charging and Battery Usage for Building Energy Efficiency. International Conference on Computer-Aided Design (ICCAD) (2014).Google Scholar
- [38].. 2016. Proactive Demand Participation of Smart Buildings in Smart Grid. IEEE Trans. Comput. 65, 5 (May 2016), 1392–1406.Google ScholarDigital Library
- [39].. 2010 Evaluation of Market Rules Using a Multi-agent System Method. IEEE Transactions on Power Systems 25, 1 (2010), 470–479.Google ScholarCross Ref
- [40].. 2007 Modeling of Suppliers Learning Behaviors in a Market Environment. International Journal of Engineering Intelligent Systems 15, 2 (2007), 115–121.Google Scholar
- [41]. 2011. Virtual Probe: A Statistical Framework for Low-Cost Silicon Characterization of Nanoscale Integrated Circuits. Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on 30, 12 (Dec 2011), 1814–1827. https://doi.org/10.1109/TCAD.2011.2164536Google ScholarDigital Library
- [42].. 2012. A Review on The Prediction of Building Energy Consumption. Renewable and Sustainable Energy Reviews 16 (2012), 3586–3592.Google ScholarCross Ref
- [43].. 2013. Peak-minimizing online EV charging. Technical Report, Purdue University. http://web.ics.purdue.eduGoogle Scholar
Index Terms
- Model-based and Data-driven Approaches for Building Automation and Control
Index terms have been assigned to the content through auto-classification.
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