To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: 1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and 2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

A distributed energy resource (DER) system uses renewable generation and energy storage to provide power and ancillary services directly to users in the distribution system, and benefits the power grid by offering a solution to the energy crisis and mitigating the waste pollution to the environment. A key challenge in implementing a DER system is how to ensure the standardization of its communication network. The goal of this paper is to study and propose a common approach to standardize the communication network in the DER system using IEC 61850 and includes a design and implementation of the communication architecture. Two prototypical testbeds for the DER system have been developed in the SMERC laboratory and at KIER, with the aim to demonstrate a case study using the proposed approach. The testbeds contain various DERs such as photovoltaics, energy management systems, and electric vehicles. The procedure of integrating the IEC 61850 standard includes the system architecture configuration, data set design, test file write-up, and deployment of the gateway/client for communication. In the data set design, the specification of logical nodes, data object names, data attributes and common data classes are explained in detail. The research results and data collected from the current testbeds indicate a quality integration of the IEC 61850 standard with stable power flow and unified communication architecture in the DER system.

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Loop-interference (LI) from relay transmission to reception reduces the channel capacity and makes the relay system unstable in full-duplex (FD) multi-input multi-output (MIMO) relay systems. conventional schemes to suppress LI still have drawbacks: 1) incur a dispensable signal-to-noise ratio (SNR) loss in low SNR regions; 2) increase the system complexity due to the requirement of weighting matrices; and 3) make insufficient practical sense due to varying signal-to-interference ratios (SIRs). In this paper, we propose a new LI suppression scheme using transmit antenna selection in order to solve the above problems. Simulation results show that the proposed scheme outperforms a conventional scheme, especially in low SNR and high SIR regions, even though the proposed scheme has a lower complexity.

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