• Journal of Environmental Science International
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• v.23 no.3
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• pp.503-510
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• 2014

To estimate the benefit of high-resolution meteorological data for building energy estimation, a building energy analysis has been conducted over Busan metropolitan areas. The heating and cooling load has been calculated at seven observational sites by using temperature, wind and relative humidity data provided by WRF model combined with the inner building data produced by American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE). The building energy shows differences 2-3% in winter and 10-30% in summer depending on locations. This result implicates that high spatiotemporal resolution of meteorological model data is significantly important for building energy analysis.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.601-605
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• 2009

The objective of this research is to develop a knowledge discovery framework which can help project teams discover useful patterns to improve energy efficient building design. This paper utilizes the technology of data mining to automatically extract concepts, interrelationships and patterns of interest from a large dataset. By applying data mining technology to the analysis of energy efficient building designs one can identify valid, useful, and previously unknown patterns of energy simulation modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1184-1191
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• 2009

Energy harvesting from environment can make the energy constrained systems such as sensor networks to sustain their lifetimes. However, environmental energy is highly variable with time, location, and other factors. Unlike the existing solutions, we solved this problem by allowing the sensor nodes with mobilizer to move in search of energy and recharge from remote energy station. In this paper we present and analyze a new harvesting aware framework for mobile sensor networks with remote energy station. The framework consists of energy model, motion control system and data transfer protocol. Among them, the objective of our data transfer protocol is to route a data packet geographically towards the target region and at the same time balance the residual energy and the link connectivity on nodes with energy harvesting. Our results along with simulation can be used for further studies and provide certain guideline for realistic development of such systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1357-1378
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• 2013

Energy constraint of wireless sensor networks makes energy saving and prolonging the network lifetime become the most important goals of routing protocols. In this paper, we propose an Energy Efficient Chain-based Routing Protocol (EECRP) for wireless sensor networks to minimize energy consumption and transmission delay. EECRP organizes sensor nodes into a set of horizontal chains and a vertical chain. Chain heads are elected based on the residual energy of nodes and distance from the header of upper level. In each horizontal chain, sensor nodes transmit their data to their own chain head based on chain routing mechanism. EECRP also adopts a chain-based data transmission mechanism for sending data packets from the chain heads to the base station. The simulation results show that EECRP outperforms LEACH, PEGASIS and ECCP in terms of network lifetime, energy consumption, number of data messages received at the base station, transmission delay and especially energy${\times}$delay metric.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.175-182
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• 2023

The purpose of this study, the trend of renewable energy, domestic and foreign renewable energy policies, and the flow of the legal system related to renewable energy location were identified, and a location analysis using public data was studied when solar energy was located. First, renewable energy is leading to energy conversion by reducing the proportion of existing fossil fuel-centered energy sources in the global trend and increasing the proportion of renewable energy, an eco-friendly energy source, and changing the institutional and market structure. Second, large-scale solar energy power plants are installed and operated in rural areas where there is no change in insolation and land prices are cheaper than in urban areas where there are many changes in insolation due to surrounding high-rise buildings and street trees. Third, if a preliminary location review is conducted using public data at this time, it will be easy to identify the optimal location for area and size calculation. Fourth, the solar energy location functional area was studied in area A, and the total area of the target area was 624.5km², with 392.7km² and 62.9% of the avoidance area where solar power cannot be located.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.277-280
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• 2009

Wind monitoring system is an absolutely-required system for assessing a performance and fatigue load of the wind energy generator in an on-shore wind energy experimental research complex. It was implemented for the purpose of monitoring the wind information measured from a meteorological tower at the monitoring house and of utilizing the measured data for the performance assessment, by using the LabVIEW program. Then, by adding the performance assessment-related data acquired from the wind energy generator during the performance assessment and the data recorder for synchronizing the data of meteorological tower, the system was implemented. Because it transmitted the data by converting the output 'RS-232' of data logger which measures the wind condition into CAN protocol, the data error rate was minimized, This paper is intended to explain the developed wind data monitoring system.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.1
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• pp.80-90
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• 2016

Thanks to superior leakage energy efficiency compared to SRAM cells, STTRAM cells are considered as a promising alternative for a memory element in on-chip caches. However, the main disadvantage of STTRAM cells is high write energy and latency. In this paper, we propose a low-cost write filter (WF) cache which resides between the load/store queue and STTRAM-based L1 data cache. To maximize efficiency of the WF cache, the line allocation and access policies are optimized for reducing energy consumption of STTRAM-based L1 data cache. By efficiently filtering the write operations in the STTRAM-based L1 data cache, our proposed WF cache reduces energy consumption of the STTRAM-based L1 data cache by up to 43.0% compared to the case without the WF cache. In addition, thanks to the fast hit latency of the WF cache, it slightly improves performance by 0.2%.

• ETRI Journal
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• v.43 no.3
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• pp.427-435
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• 2021

Data filtering is an essential task for improving the energy efficiency of radiofrequency identification (RFID) networks. Among various energy-efficient approaches, clustering-based data filtering is considered to be the most effective solution because data from cluster members can be filtered at cluster heads before being sent to base stations. However, this approach quickly depletes the energy of cluster heads. Furthermore, most previous studies have assumed that readers are fixed and interrogate mobile tags in a workspace. However, there are several applications in which readers are mobile and interrogate fixed tags in a specific area. This article proposes a model for dynamic clustering-based data filtering (DCDF) in mobile RFID networks, where mobile readers are re-clustered periodically and the cluster head role is rotated among the members of each cluster. Simulation results show that DCDF is effective in terms of balancing energy consumption among readers and prolonging the lifetime of the mobile RFID networks.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.159-164
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• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.