A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human–machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.

Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.

The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outdoor environment has yet to be determined. To address this problem, this study builds a LoRa testbed from which we measure the propagation properties of radio signals in a combined environment. Then, we establish a simplified path loss model for LoRa PHY and compute the communication range. Next, national regulations enforce the use of the Listen-Before-Talk (LBT) scheduling scheme, but its performance has never been examined. This paper enumerates the impacts of LBT, threshold values, back-off algorithms, and application parameters on LoRa Medium Access Control (MAC) performance. Last, the application parameters required by the smart energy scenario influence LoRa performance, but their impacts have been scarcely reported. Based on the application parameters and experimental results, this paper proposes a strategy to deploy LoRa gateway(s) and nodes on the campus. To the best of the authors’ knowledge, this is the first work that considers performance issues when establishing a LoRa network on a real-world application testbed.

It has been found that a cloud building energy management system (BEMS) alone cannot support increasing numbers of end devices (e.g., energy equipment and IoT devices) and emerging energy services efficiently. To resolve these limitations, this paper proposes Fog BEMS, which applies an emerging fog computing concept to a BEMS. Fog computing places small computing resources (fog nodes) just next to end devices, and these nodes process data in real time and manage local contexts. In this way, the BEMS becomes distributed and scalable. However, existing fog computing models have barely considered scenarios where many end devices and fog nodes are deployed and interconnected. That is, they do not scale up and cannot be applied to scalable applications like BEMS. To solve the problem, this paper (i) designs a fog network where a list of functionally heterogeneous nodes is deployed in a hierarchy for collaboration and (ii) designs an agent-based, modular programming model that eases the development and management of computing services at a fog node. We develop a prototype of a fog node and build a real-world testbed on a campus to demonstrate the feasibility of the proposed system. We also conduct experiments, and results show that Fog BEMS is scalable enough for a node to connect up to 900 devices and that network traffic is reduced by 27.22–97.63%, with varying numbers of end devices.

Direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications have been popularly considered for safe driving of manned or unmanned vehicles. The V2I communication is better than the V2V communication for propagating safety messages at critical regions like intersections where the safety messages must be delivered to surround vehicles with low latency and loss, since transmitters as infrastructure can have line of sight to the receiver vehicles and control wireless medium access in a centralized manner unlike V2V. Long-Term Evolution (LTE) cellular networks are rapidly deployed in the world with explosively increasing mobile traffic. As many automobile manufacturers choose LTE on-board devices for telematics, the LTE system can be utilized also for safety purposes instead of 802.11p/WAVE based roadside units (RSUs). Previous literatures have studied mostly current LTE system analysis in aspect of theoretical network capacity and end-to-end delay to investigate feasibility of V2I communication. In this paper, we propose new enhancement of a current LTE system specified by 3rd-Generation Partnership Project (3GPP) LTE standards while addressing major delay challenges. From simulation, we confirm that our three key solutions can reduce end-to-end delay effectively in the LTE system to satisfy requirements of safety message delivery.

The Internet of Things (IoT) technology is rapidly being applied to real life, but the application of a corresponding secure and convenient authentication method is still in significant challenge. So far, pattern, password and fingerprint authentication are the most used methods, but it is important to address various security vulnerabilities and limitations of these approaches. In the case of fingerprint recognition, additional hardware such as a fingerprint scanner is required, which causes cost issues and could be vulnerable to fingerprint theft. To solve this problem, this paper proposes a model that uses both biometric and behavioral authentication at the same time. This method exploits the biometric authentication that measures the length of the contact region that occurs when three fingers are placed side by side on the touch screen or pad. In addition, it utilizes the behavioral authentication itself using three-finger L-shape touch, as well as secure geometric information generated by smart watch such as acceleration sensors. Therefore, this proposed model will be useful to implement more secure, rapid and user-friendly way of authentication in many practical busy and buzzling field where deal with sensitive private information.