Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multi-constraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.

For decades, ASHRAE Standard 55 has been using the Fanger's predicted mean vote (PMV) model to evaluate the indoor thermal comfort satisfaction. However, this canonical model has drawbacks in both data inadequacy and lack of inputs from test subjects. In this paper, we propose a learning-based solution for thermal comfort modeling via the emerging machine learning techniques and Internet of Things-based pervasive sensing technologies. First, we build an intelligent thermal comfort management (iTCM) system. It adopts the wireless sensor network to collect environmental data and utilizes the wearable device for vital sign monitoring. In addition, a cloud-based back-end system, with cost efficient deployment fees, is developed for data management and analysis. Second, we implement a black-box neural network (NN), namely the intelligent thermal comfort NN (ITCNN). To evaluate the performance of ITCNN, we compare it with the PMV model, three traditional white-box machine learning approaches and three classical black-box machine learning methods. Our preliminary results show that four black-box methods achieve better performance than the PMV model and the three white-box approaches. The ITCNN achieves the best performance and outperforms the PMV model by on average 13.1% and up to 17.8%. Third, with the iTCM system, we demonstrate a novel deep reinforcement learning-based application by encouraging human behavioral changes to form energy-saving habits for greener, smarter, and healthier building. Finally, we discuss the limitations of this paper and present the plan for our future research.

In this paper, we proposed to evaluate the optimal phasor measurement unit (PMU) placement based on the sum of variance (SV) of the robust estimators. Variance is traditionally used as a measure of the quality of estimates. In this paper, after satisfying the requirements of the minimum number of PMUs, maximum measurement redundancy, and observability, the placements obtained are further distinguished on the basis of the variance of the estimated states. Both the weighted least squares (WLS) and robust estimators are considered. Examples on the IEEE 30-bus system using least absolute value (LAV) estimation are given. The covariance of the state estimates from the WLS can be calculated using the existing formulas. However, there is no equivalent formula for the robust LAV estimator. A formula is derived in this paper using influence function to approximately calculate the covariance matrix of the state estimates from the robust LAV estimator. The optimum placement, i.e., the placement with the smallest SV, can be selected.

This article proposes a method for optimal placement of phasor measurement units (PMUs) to improve measurement redundancy distribution in a system under normal operating condition or in presence of contingency. The minimum number of PMUs is determined using integer linear programming method, after which the measurement redundancy is optimized using a novel model to represent the redundancy level of each bus. Weight parameters are assigned to auxiliary variables, which enables control of priority of different redundancy level. The impact of zero injection bus is integrated into the model, as well as the channel limitation of PMUs and existing measurements. Besides, contingency is modeled in a generalized form, which enables N-1 or N-2 contingency analysis. The proposed method is tested on various IEEE systems, and the results show that the number of PMUs obtained by the proposed method is further reduced comparing with other methods, while with a highly improved measurement redundancy.

Thermal comfort is a key consideration in smart buildings and a number of comfort models are available nowadays to evaluate the comfort level of occupants. However, the models are often complex and hardly interpretable for the developers and operators. Indeed, the model interpretations are beneficial in multifold such as for system inspection and optimization. In this paper, we propose an interpretable thermal comfort system to introduce interpretability to any black-box comfort models. First, we focus on the relationship between a model’s input features and output comfort level. The feature impact on comfort is investigated and the impact patterns are shown to be diverse for different features. Second, we unveil the model mechanisms about the data processing inside the model by building the model surrogates based on the interpretable machine learning algorithms. The surrogates offer outstanding fidelity for simulating the actual model mechanisms and the interpretations based on the surrogates are intuitive and informative. Our interpretable comfort system can be integrated with the existing building management systems. Accordingly, we can ease building owner’s concerns about adopting new black-box technologies and enable various smart building applications like smart energy management.

The increased focus on sustainability in response to climate change has given rise to many new initiatives to meet the rise in building load demand. The concept of distributed energy resources (DER) and optimal control of supply to meet power demands in buildings have resulted in growing interest to adopt microgrids for a precinct or a university campus. In this paper, a model for an actual physical microgrid has been constructed in OPAL-RT for real-time simulation studies. The load demands for SIT@NYP campus and its weather data are collected to serve as input to run on the digital twin model of DERs of the microgrid. The dynamic response of the microgrid model in response to fluctuations in power generation due to intermittent solar PV generation and load demands are examined via real-time simulation studies and compared with the response of the physical assets. It is observed that the simulation results match closely to the performance of the actual physical asset. As such, the developed microgrid model offers plug-and-play capability, which will allow power providers to better plan for on-site deployment of renewable energy sources and energy storage to match the expected building energy demand.