Guest Editorial: Special Issue on Smart Homes, Buildings and Infrastructures
As the complexity of Cyber-Physical Systems (CPS) increases, it becomes more and more challenging to ensure CPS reliability, especially in the presence of software and/or physical failures. The Simplex architecture is shown to be an efficient tool to address software failures in such systems. When physical failures exist, however, Simplex may not function correctly, because physical failures could change the system dynamics and the original Simplex design may not work for the new faulty system. To address concurrent software and physical failures, this paper presents the RSimplex architecture, which integrates the robust fault-tolerant control (RFTC) techniques into the Simplex architecture. It includes the uncertainty monitor, the high-performance controller (HPC), the robust high-assurance controller (RHAC), and the decision logic that triggers the switch of the controllers. Based on the uncertainty monitor of physical failures, we introduce a monitor-based switching rule in the decision logic in addition to the traditional stability-envelope-based rule. The RHAC is designed based on robust fault-tolerant controllers. We show that RSimplex can efficiently handle a class of software and physical failures.
The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally-generated solar energy. In this paper, we present two machine learning techniques to predict solar power from publicly-available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service, which automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a ``black box'' approach that requires only i) a site's geographic location and ii) a minimal amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site. We evaluate the accuracy of SolarCast's different algorithms on two publicly available datasets, each containing over one hundred rooftop deployments with a variety of attributes, e.g., climate, tilt, orientation, etc. We show that SolarCast learns a more accurate model using much less data (~1 month) than prior SVM-based approaches, which require ~3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy-efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a ``sunny" load scheduler, which schedules a dryer's energy usage to maximally align with a home's solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ~5 hours) in the dryer's start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.
This paper addresses the application of real-time scheduling to the reduction of the peak load of power consumption generated by electric loads in Cyber-Physical Energy Systems (CPES). The goal is to reduce the peak load while achieving a desired Quality of Service of the physical system under control. The considered physical processes are characterized by integrator dynamics and modelled as sporadic real-time activities. Timing constraints are obtained from physical parameters, and are used to manage the activation of electric loads by a real-time scheduling algorithm. As a main contribution, an algorithm derived from the multi-processor real-time scheduling domain is proposed to efficiently deal with an high number of physical processes (i.e., electric loads), making its scalability suitable for large CPES, such as smart energy grids. The cyber-physical nature of the proposed method arises from the tight interaction between the physical processes operated by the electric loads, and the applied scheduling. To allow the use of the proposed approach in practical applications, modelling approximations and uncertainties on physical parameters are explicitly included in the model. An adaptive control strategy is proposed to guarantee the requirements on physical values under control in presence of modelling and measurement uncertainties. The compensation for such uncertainties is done by dynamically adapting the values of timing parameters used by the scheduler. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads. The performance of the method is evaluated by means of physically accurate simulations of Heating-Ventilation Air-Conditioning (HVAC) systems, showing a remarkable reduction of the peak load and a robust enforcement of the desired physical requirements.
Engine control applications include functions that need to be executed at specific rotation angles of the crankshaft. The tasks performing these functions are activated at variable rates and are programmed to be adaptive with respect to the rotation speed of the engine to avoid overloading the CPU. Simplified control implementations are used at high speeds, for example reducing the number of fuel injections or the complexity of the computations. Such different control implementations define execution modes with different execution times for different ranges of the rotation speed. The selection of the switching speeds for the operating modes of such tasks is an optimization problem, consisting in determining the optimal transition speeds that maximize the engine performance while guaranteeing schedulability. This paper presents three methods for tackling such an optimization problem under a set of assumptions about the performance metrics: two heuristics and a branch and bound method that guarantees finding the optimal solution within a given speed granularity. In addition, a simple method to compute a performance upper bound is presented. The approach and the hypothesis are validated using a Simulink model of the engine and the computational tasks, considering the engine efficiency and the production of pollutants (NO2) as metrics of interest. Simulation experiments show that the performance of proposed heuristics is quite close to the one of the upper bound and the optimum within a finite granularity.
Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks, and system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient- and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and efficiency of our proposed model.
With the ongoing development of sensor devices and network techniques, big data is being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data poses a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves the super performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data since it uses the nonlinear function as the encoder. In this paper, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, some experiments are conducted to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results indicate that the proposed model performs better than the conventional deep computation model and the denoising deep computation model for the classification and the restoration for the low-quality data in cyber-physical systems.
Automotive functionalities typically consist of a large set of periodic/cyclic tasks scheduled under a time-triggered operating system (OS), and a large fraction of them are feedback control applications. OSEK/VDX is a common time-triggered automotive OS that offers preemptive periodic schedules supporting a pre-configured set of periods. The feedback controllers implemented onto such OSEK/VDX-compliant systems need to use one of the pre-configured (sampling) periods. A shorter period is often desired for a feedback controller for higher control performance, and on the other hand, this implies a higher processor load. For a given performance requirement, the longest sampling period that meets this requirement is the optimal one. Given a limited set of pre-configured periods, such optimal sampling periods are often not available, and the practice is to choose a shorter available period -- leading to a higher processor load. To address this, we propose a controller that cyclically switches among the available periods, thereby leading to an average sampling period closer to the optimal one. This way, we reduce the processor load and are able to pack more control applications on the same processor. The main challenge in this paper is the design of such controllers that takes into account such cyclic switching of sampling periods (i.e., use non-uniform sampling) and meets specified performance requirements (in settling time, which is the key metric for many real-time control applications and more difficult to optimize than quadratic cost) and system constraints (e.g., input saturation). Such a non-convex constrained controller optimization problem as raised in the OS-aware automotive systems design has not been addressed in the control theory and a new approach based on adaptively parameterized particle swarm optimization (PSO) is proposed to solve it.
Decisions on how best to optimize todays energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive. Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. We consider the problem of data-driven end-user demand response and peak power reduction for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies, synthesizing DR control actions, and reducing the peak power consumption. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45, 000 in DR revenue. A data predictive control with regression trees (DPCRT) algorithm, is also presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the buildings facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penns campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAEs benchmarking data-set for energy prediction.
In this paper, we perform a comprehensive survey to the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs), the power architecture with multi-bus characteristics, different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings, the communication technologies and network architectures, and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.
Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step towards better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this paper, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semi-supervised approach combining hidden Markov models (HMM) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.