Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems

Behl, Madhur; Jain, Achin; Mangharam, Rahul

doi:10.1109/iccps.2016.7479093

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…Behl et al proposed an open source e-CPSs, DR-Advisor, which also allows data-driven modeling and control with rule-based algorithms. Based on a comparison with DOE commercial reference buildings, their system showed a 17% energy saving [18]. For the data-driven thermal control studies, researchers focus on converting physically captured data to system operation schedule and settings.…”

Section: Energy Management and Cyber-physical Systemmentioning

confidence: 99%

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Wang

Hong

et al. 2019

Applied Energy

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With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging BM Baseline model

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Section: Energy Management and Cyber-physical Systemmentioning

confidence: 99%

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Wang

Hong

et al. 2019

Applied Energy

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“…These types of techniques are the pre-cursors to a distributed environment with predictive resource capabilities (Fig.1) [3] used a machine-learning technique called Learning-based Model Predictive Control that combined models with statistics to estimate occupancy and heating load based only on temperature measurements. To compensate for heating by occupancy control action chosen (AC on/off), [4] demonstrated a demand-response strategy synthesis that uses regression trees to partition the data space into small, manageable regions and then to partition the partitions until the data spaces can have simple models to fit them (easier for humans to interpret). Predictor variables are disturbances (weather, temperature) and controllable actions.…”

Section: A Dr and Der Local Availability And Verificationmentioning

confidence: 99%

Integrated multi-scale data analytics and machine learning for the distribution grid

Stewart

Top

Chertkov

et al. 2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors-such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals-such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machinelearning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

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“…The results in this paper are presented for this single zone, however, the algorithms described next are easily scalable to multiple zones. In [1], we have successfully modeled a 12 storey, 70 zone building with our data-driven algorithms. The focus of this work is on comparing the performance of DPC with MPC and using a single zone suffices for that comparison as it eliminates any concomitants in the performance comparison.…”

Section: Modelingmentioning

confidence: 99%

Data Predictive Control for building energy management

Jain

Behl

Mangharam

2017

2017 American Control Conference (ACC)

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Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our results demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high. Abstract-Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our simulations demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R 2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high.

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Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems

Cited by 18 publications

References 14 publications

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

Integrated multi-scale data analytics and machine learning for the distribution grid

Data Predictive Control for building energy management

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