Hengyang Zhao

Learning-based occupancy behavior detection for smart buildings

In this article, we propose a novel method to detect the occupancy behavior of a building through the temperature and/or possible heat source information, which can be used for energy reduction, security monitoring for emerging smart buildings. Our work is based on a realistic building simulation program, EnergyPlus, from Department of Energy. EnergyPlus can model the various time-series inputs to a building such as ambient temperature, heating, ventilation, and air-conditioning (HVAC) inputs, power consumption of electronic equipment, lighting and number of occupants in a room sampled in each hour and produce resulting temperature traces of zones (rooms). The new approach is based on a learning based approach in which a recurrent neutral network (RNN) is trained to detect the number of people in a room based on the room temperature and other information such as ambient temperature, and other related heat sources. We applied the Elmans recurrent neural network (ELNN), which has local feedbacks in each layer. We use an empirical formula to calculate the RNN layer number and layer size to configure RNN architecture to avoid overfitting and under-fitting problems. Experimental results from a case study of a 5-zone building show that ELNN can lead to very accurate occupancy behavior estimation. The error level, in terms of number of people, can be as low as 0.0056 on average and 0.288 at maximum when we consider ambient, room temperatures and HVAC powers as detectable information. Without knowing HVAC powers, estimation error can still be 0.044 on average, and only 0.71% estimated points have errors greater than 0.5.

Parallel GMRES solver for fast analysis of large linear dynamic systems on GPU platforms

In this paper, we propose an efficient parallel dynamic linear solver, called GPU-GMRES, for transient analysis of large linear dynamic systems such as large power grid networks. The new method is based on the preconditioned generalized minimum residual (GMRES) iterative method implemented on heterogeneous CPU-GPU platforms. The new solver is very robust and can be applied to power grids with different structures as well as for general analysis problems for large linear dynamic systems with asymmetric matrices. The proposed GPU-GMRES solver adopts the very general and robust incomplete LU based preconditioner. We show that by properly selecting the right amount of fill-ins in the incomplete LU factors, a good trade-off between GPU efficiency and convergence rate can be achieved for the best overall performance. Such tunable feature can make this algorithm very adaptive to different problems. GPU-GMRES solver properly partitions the major computing tasks in GMRES solver to minimize the data traffic between CPU and GPUs to enhance performance of the proposed method. Furthermore, we propose a new fast parallel sparse matrix-vector (SpMV) multiplication algorithm to further accelerate the GPU-GMRES solver. The new algorithm, called segSpMV, can enjoy full coalesced memory access compared to existing approaches. To further improve the scalability and efficiency, segSpMV method is further extended to multi-GPU platforms, which leads to more scalable and faster multi-GPU GMRES solver. Experimental results on the set of the published IBM benchmark circuits and mesh-structured power grid networks show that the GPU-GMRES solver can deliver order of magnitudes speedup over the direct LU solver, UMFPACK. The resulting multi-GPU-GMRES can also deliver 3-12×speedup over the CPU implementation of the same GMRES method on transient analysis. HighlightsThe paper proposed a novel parallel GMRES based iterative solver on GPU platforms.It has two key techniques: one is the GPU-enabled GMRES solver and the second is a novel sparse vector and matrix multiplication (spMV) algorithm implemented on GPUs.We also implemented the multi-GPU version of the proposed segSpMV algortithm for further speedup.The resulting parallel GMRES solver leads to order of magnitudes speedup over the CPU version of the iterative solvers and the director LU factorization solvers.

Invited - Cross-layer modeling and optimization for electromigration induced reliability

In this paper, we propose a new approach for cross-layer electromigration (EM) induced reliability modeling and optimization at physics, system and datacenter levels. We consider a recently proposed physics-based electromigration (EM) reliability model to predict the EM reliability of full-chip power grid networks for long-term failures. We show how the new physics-based dynamic EM model at the physics level can be abstracted at the system level and even at the datacenter level. Our datacenter system-level power model is based on the BigHouse simulator. To speed up the online optimization for energy in a datacenter, we propose a new combined datacenter power and reliability compact model using a learning based approach in which a feed-forward neural network (FNN) is trained to predict energy and long term reliability for each processor under datacenter scheduling and workloads. To optimize the energy and reliability of a datacenter, we apply the efficient adaptive Q-learning based reinforcement learning method. Experimental results show that the proposed compact models for the datacenter system trained with different workloads under different cluster power modes and scheduling policies are able to build accurate energy and lifetime. Moreover, the proposed optimization method effectively manages and optimizes datacenter energy subject to reliability, given power budget and performance.

Learning Based Compact Thermal Modeling for Energy-Efficient Smart Building Management: (invited)

In this article, we propose a new behavioral thermal modeling method for fast building performance analysis, which is critical for energy-efficient smart building control and management. The new approach is based on two recurrent neutral network architecture to obtain the compact nonlinear thermal models for complicated building. We start with a more realistic building simulation program, EnergyPlus, from Department of Energy, to model some practical buildings such as office buildings and data centers. EnergyPlus can model the various time-series inputs to a building such as ambient temperature, heating, ventilation, and air-conditioning (HVAC) inputs, power consumption of electronic equipment, lighting and number of occupants in a room sampled in each hour and produce resulting temperature traces of zones (rooms). In this work, we apply two recurrent neural network (RNN) architectures to build the non-linear compact thermal model of the building: one is non-linear state-space RNN architecture (NLSS), which has global feedbacks, and the other one is Elmans RNN architecture (ELNN), which has local feedbacks in each layer. We give a simple formula to calculate the RNN layer number, layer size to configure RNN architecture to avoid overfitting and underfitting problems. A cross-validation based training technique is further applied to improve predictable accuracy of models. Experimental results from a case study of three buildings show that ELNN and NLSS can both build very accurate building thermal models for the 2-zone and 5-zone building cases: both of them have average errors from around 1% to 1.5% for the two buildings. For the more complex 6-zone building case, ELNN outperforms NLSS with maximum errors 16% against 23%. But both methods have 2.2% average errors.

Thermal modeling for energy-efficient smart building with advanced overfitting mitigation technique

Building energy accounts large amount of the total energy consumption, and smart building energy control leads to high energy efficiency and significant energy savings. A compact and accurate building thermal model is important for designing the efficient energy control system. In this paper, we propose an accurate thermal behavior modeling technique for general and complicated buildings. This new modeling technique builds compact thermal model by system identification using temperature and power data obtained from EnergyPlus software, which can provide realistic temperature, weather and power data for buildings. In order to make the best use of data from EnergyPlus and avoid the overfitting problem associated with the system identification method, a cross-validation technique is employed to generate multiple thermal models to find the optimal model order. The final model is then generated by performing a regular system identification using the previously selected order. Experimental results from a case study of a 5-zone building have shown that the proposed method is able to find the optimal model order, and the building models built by the proposed method can achieve 1-3% average errors and less than 10-18% maximum errors for the estimation of zone temperatures for about a one year period.

Thermal-Sensor-Based Occupancy Detection for Smart Buildings Using Machine-Learning Methods

In this article, we propose a novel approach to detect the occupancy behavior of a building through the temperature and/or possible heat source information. The new method can be used for energy reduction and security monitoring for emerging smart buildings. Our work is based on a building simulation program, EnergyPlus, from the Department of Energy. EnergyPlus can model various time-series inputs to a building such as ambient temperature; heating, ventilation, and air-conditioning (HVAC) inputs; power consumption of electronic equipment; lighting; and number of occupants in a room, sampled each hour, and produce resulting temperature traces of zones (rooms). Two machine-learning-based approaches for detecting human occupancy of a smart building are applied herein, namely support vector regression (SVR) and recurrent neural network (RNN). Experimental results with SVR show that the four-feature model provides accurate detection rates, giving a 0.638 average error and 5.32% error rate, and the five-feature model delivers a 0.317 average error and 2.64% error rate. This indicates that SVR is a viable option for occupancy detection. In the RNN method, Elman’s RNN can estimate occupancy information of each room of a building with high accuracy. It has local feedback in each layer and, for a five-zone building, it is very accurate for occupancy behavior estimation. The error level, in terms of number of people, can be as low as 0.0056 on average and 0.288 at maximum, considering ambient, room temperatures, and HVAC powers as detectable information. Without knowing HVAC powers, the estimation error can still be 0.044 on average, and only 0.71% estimated points have errors greater than 0.5. Our article further shows that both methods deliver similar accuracy in the occupancy detection. But the SVR model is more stable for adding or removing features of the system, while the RNN method can deliver more accuracy when the features used in the model do not change a lot.

Recovery-aware proactive TSV repair for electromigration in 3D ICs

Electromigration (EM) becomes a major reliability concern in three-dimensional integrated-circuits (3D ICs). To mitigate this problem, a typical solution is to use TSV redundancy in a reactive manner, maintaining the operability of a 3D chip in the presence of EM failures by detecting and replacing faulty TSVs with spares. In this work, we explore an alternative, more preferred approach to enhance the EM-related lifetime reliability of TSV grid, in which redundancy is used proactively to allow non-faulty TSVs to be temporarily deactivated. In this way, EM wear-out can be reversed by exploiting its recovery property. Applied to 3D benchmark designs, the recovery-aware proactive repair approach increases EM-related lifetime reliability (measured in mean-time-to-failure) of the entire TSV grid by up to 12X relative to the conventional reactive method, with less area overhead.

Online Unusual Behavior Detection for Temperature Sensor Networks

In modern smart building climate control systems, accurate detection of unusual behavior in temperature sensors (outliers) can help reduce or prevent waste of energy consumption in a Heating, Ventilation and Air Conditioning (HVAC) system. In this work, we propose online learning-distance based outlier detection method. In the new method, we train and tune a multilayer neural network to learn a nonlinear distance function from historical building operation data and detect outliers according to the calculated distance. The online detection method is less computational expensive than the offline version. By gradually including new and drop old building operation record, the new method is capable to adjust the underlying distance function on-the-fly. The converging speed of the learned distance function and tuning difficulty of network training are also discussed. The proposed online outlier detection method can work in an unsupervised manner except requiring only one data-specific parameter. In the experiments of two simulated buildings, the data-specific parameter can be chosen from a relatively wide range, which allows less tuning effort, to achieve good online detection precision and recall.