"Fig. 1. SID model development procedure Details about how to use the training data to obtain a SID model is described in   and is briefly summarized in Fig. 1. In this figure, U is training inputs, Y is training outputs data, PSD is power spectral density model for inputs, and CPSD is cross power spectral density model for input and output. "
[Show abstract][Hide abstract] ABSTRACT: Buildings, consuming over 70% of the electricity in the U.S., play significant roles in smart grid infrastructure. The automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy consumption and demand, as well as improve the resilience to power disruptions. In order to achieve such automatic operation, high fidelity and computationally efficiency building energy forecasting models under different weather and operation conditions are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control operation. However, typical black box models often require long training period and are bounded to weather and operation conditions during the training period. On the other hand, creating a grey box model often requires long calculation time due to parameter optimization process and expert knowledge during the model structure determining and simplification process. An earlier study by the authors proposed a system identification approach to develop computationally efficient and accurate building energy forecasting models. This paper attempts to extend this early study and to quantitatively evaluate how the most important characteristics of a building energy system: its nonlinearity and response time, affect the system identification process and model accuracy. Two commercial building: a small-size and a medium-size commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and validation. The system identification method proposed in the early study is applied to these two buildings that have varying nonlinearity and response time. Adaption of the proposed system identification method based on systems??? nonlinearity and response time is proposed in this study. The energy forecasting results demonstrate that the adaption is capable of significantly improve the performan- e of the system identification model.
2015 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), Seattle, WA; 04/2015
"(C) and (D) Learning the dimensionality and dynamics, via subspace identification  of a linear neural network of size N = 5000 from spontaneous noise driven activity. The low-rank connectivity of the network forces the system to lie in a K = 6 dimensional subspace. "
"The above derivation shows that the relationship between the outputs of two sensors is precisely captured by the ARX model, which is defined by of coefficients α i and β i . This ARX model (α i and β i ) can be acquired by first storing the output pairs, y p and y q over a certain period of time when sensors work under normal conditions and then α i and β i are calculated from the stored data through standard least square calculations ,  or through the iterative Burg's method . Even if the historical data are corrupted by (zero mean) Gaussian noise, these training methods are able to extract accurate model coefficients. "
[Show abstract][Hide abstract] ABSTRACT: Compact and low-cost sensors used in wireless sensor networks are vulnerable to deterioration and failure. As the number and scale of sensor deployments grow, the failure of sensors becomes an increasingly paramount issue. This paper presents a distributed, reference-free fault detection algorithm that is based on local pair-wise verification between sensors monitoring the same physical system. Specifically, a linear relationship is shown to exist between the outputs of a pair of sensors measuring the same system. Using this relationship, faulty sensors may be detected within subsystems of the global system. Moreover, faulty sensors suffering from sparse spikes in their measurements can be identified with spike magnitudes and times accurately estimated. An appealing feature of the proposed method is that the need for reference sensors and complete knowledge of the system input are not required. Due to the pair-wise nature of the proposed algorithm, it can also be performed in a completely decentralized fashion. This ensures the method can be scaled to large sensor networks and lead to significant energy savings derived from reduced wireless communication compared to centralized approaches.
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