• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.10 no.6
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• pp.695-701
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• 1998

Researches on unused energy are being continued because of the limited fossil fuel and the destruction of environment. Therefore this study was peformed as follows. The collectable amount of exhausted heat for an air-conditioning was calculated by the subway thermal environment prediction program. And the electric power needed by conventional heat source equipments was compared with one by unused heat source equipments when the exhausted heat was used by heat pump in heating and hot water supplying. The results are summarized as follows; 1) Forced ventilation should be conducted to keep optimal temperature in subway tunnel in summer as well as in winter. According to the simulation, temperature in tunnel was higher than that on the ground in summer when the forced ventilation was conducted only in winter. 2) Ventilating time should be calculated out to the optimal condition for not only saving power of ventilation fan but reusing exhausted heat. By the simulation, it is certain that the exhausted heat should be eliminated in air-conditioning time. 3) The use of exhausted heat source heat pump could save 8% of electric power per hour in comparison with existing heat pump. It was based on a present heat generation and traffic for ventilating time of general air-conditioning, but could be different by ventilating time. 4) As the traffic increases up to 1.5 or 2 times, electric power consumption of the conventional heat pump increases to 11% or 13.5% per mean hour in comparison with that of the exhausted heat source heat pump, though all-day ventilation.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.71-77
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• 2015

Upcoming ground-breaking applications for always-on tiny interconnected devices steadily demand two-fold features of processor cores: aggressively low power consumption and enhanced performance. We propose implementation of a novel superscalar low-power processor core with a low supply voltage. The core implements intra-core low-power microarchitecture with minimal performance degradation in instruction fetch, branch prediction, scheduling, and execution units. The inter-core lockstep not only detects malfunctions during low-voltage operation but also carries out software-based recovery. The chip incorporates a pair of cores, high-speed memory, and peripheral interfaces to be implemented with a 65nm node. The processor core consumes only 24mW at 350MHz and 0.68V, resulting in power efficiency of $80{\mu}W/MHz$. The operating frequency of the core reaches 850MHz at 1.2V.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.289-292
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• 2008

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of OpenRISC processor and a clock gating algorithm using ODC (Observability Don't Care) operation for a low-power processor. The branch prediction algorithm has a structure using BTB(Branch Target Buffer) and 4-way set associative cache has lower miss rate than direct-mapped cache. The clock gating algorithm reduces dynamic power consumption. As a result of estimation of performance and dynamic power, the performance of the OpenRISC processor using the proposed algorithm is improved about 8.9% and dynamic power of the processor using samsung $0.18{\mu}m$ technology library is reduced by 13.9%.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• The Journal of Fisheries Business Administration
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• v.36 no.3 s.69
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• pp.89-117
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• 2005

Currently, the tax - exempted vessel fuel is provided for commercial fishing in order to increase the competitive power of fishery production thorough the National Federation of Fisheries Cooperatives. The National Federation of Fisheries Cooperatives should predict the exact amount of fuel consumption for fishing every year to request the fuel from the government. Unfortunately, there is no sophisticated model to predict the tax - exempted vessel fuel consumption. In 2003, the consumption of the tax- exempted vessel fuel was only $25.1\%$ of the estimation amount by the National Federation of Fisheries Cooperatives. This causes an inefficiency in the petroleum management. Moreover, we need some data such as the annual average fishing hours, fishing days and fishing behavior to adopt a new policy regarding fishing. Up to now, the data have been obtained by survey with response in the fishery field. In the most case, we have a small number of data because we spend so much time and money consuming for collecting fishing data. As a result, the level of confidence of the data is associated with the sample size and normally low. In order to achieve more accurate data, we need to develope an efficient method for collecting fishing data. In this research, we proposed a new method to predict the tax- exempted vessel fuel consumption more exactly. The prediction results from the proposed method has been compared with the results from the current method. According to the results in this research, the method proposed here produced much better accuracy than the current method. In addition, we also proposed in the paper for collecting fishing data of the annual average fishing hours using the tax - exempted vessel fuel consumption and the gasoline consumption of vessel engine. The fishing data obtained by using the method proposed in this research could be much more efficient and accurate because it doesn't need to estimate from survey sample data.

• Journal of IKEEE
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• v.8 no.1 s.14
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• pp.63-72
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• 2004

The supply voltage has been drop rapidly and the total length of the wire increased exponentially in the nanometer SoC design environment. The ideal supply voltage was dropped sharply by the resistance and parasitic devices which stayed on the kilometers-long wire length. Even worse, it could severely affect the functional behavior of the block of the design. To analyze the effects of the long wire of the SoC while maintaining the accuracy, the modeling of the current and the RC conversion of the parasitic techniques are researched and applied. By these modeling and conversion, the multi-million gates HDTV Chipset can be analyzed within a day. The benchmark analysis of the HDTV SoC showed the superiority to the conventional methods in performance and accuracy.

• Proceedings of the KIEE Conference
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• summer
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• pp.737-739
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• 2004

The domestic power system established with Cost-Based-Pricing(CBP) from April 2001. The system is a uniform pricing system. System Operator(50) establishes a Price Setting Schedule by the prediction of consumption and the presented bid price(generation cost) of the generation utility. But the Price Setting Schedule doesn't take account of the constraint of the system. This cause a transmission congestion, constrained-on generation and constrained-off generation. This Paper search the way of the increasing efficiency of domestic power system through the redemption of congestion charge.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.801-809
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• 2007

LDPC Low Density Parity Check) code, which is an error correcting code determined to be applied to the 4th generation mobile communication systems, requires a heavy computational complexity due to iterative decodings to achieve a high BER performance. This paper proposes an algorithm to reduce the number of decoding iterations to increase performance of the decoder in decoding latency and power consumption. Measuring changes between the current decoded LLR values and previous ones, the proposed algorithm predicts directions of the value changes. Based on the prediction, the algorithm inverts the sign bits of the LLR values to speed up convergence, which means parity check equation is satisfied. Simulation results show that the number of iterations has been reduced by about 33% without BER performance degradation in the proposed decoder, and the power consumption has also been decreased in proportional to the amount of the reduced decoding iterations.