• Environmental Engineering Research
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• v.21 no.4
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• pp.355-364
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• 2016

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.453-461
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• 2010

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of embedded RISC core and a clock-gating algorithm using ODC (Observability Don't Care) operation to improve the power consumption of the core. 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. Pseudo-LRU Policy, which is one of the Line Replacement Policies, is used for decreasing the number of bits that store LRU value. The clock gating algorithm reduces dynamic power consumption. As a result of estimation of performance and dynamic power, the performance of the OpenRISC core applied the proposed architecture is improved about 29% and dynamic power of the core using Chartered $0.18{\mu}m$ technology library is reduced by 16%.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.491-497
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• 2015

In this paper a method of design and Implementation of RFID based control system for reducing standby power consumption at the power outlet is described. The system is composed of a RF controlled power outlet having relay and an active RFID tag communicating with the RF reader module controlling the relay. When the tag carried by human approaches to the RF reader the reader recognizes the tag and switch off the relay based on the RSSI level measurement. A low power packet prediction algorithm has been used to decrease the DC power consumption at both the tag and the RF reader. The result of experiment shows that successful operation of the relay control has been obtained while low power operation of the tag and the reader is achieved using above algorithm. Also setting the distance between the reader and the tag by controlling transmission power of the tag and adjusting the duty cycle of the packet waiting time when the reader is in idle state allows us to reduce DC power consumption at both the reader and the tag.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.488-498
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• 2016

Much of the information is stored as data, research has been activated for analyzing the data and predicting the special circumstances. In the case of power data, the studies, such as research of renewable energy utilization, power prediction depending on site characteristics, smart grid, and micro-grid, is actively in progress. In this paper, we propose a power prediction model using the substation environment data. In this case, we try to verify the power prediction result to reflect the multiple arguments on the power and weather data, rather than a simple power data. The validation process is the effect of multiple factors compared to other two methods, one of power prediction result considering power data and the other result using power pattern data that have been made in the similar weather data. Our system shows that it can achieve max prediction error of less than 15%.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.65-72
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• 2018

In this paper, we used LSTM as a method to detect abnormal behavior of motors. We fixed the high layout size to 1 and changed the range of the input values and the neural network structure to see what change in power consumption prediction. Now, as the fourth industrial revolution era, smart factories are attracting attention. All the physical actions of smart factories are done using motors. Continuous monitoring of motor malfunctions helps to detect malfunctions and efficient operation. However, it is difficult to acquire the power consumption constantly due to the influence of the noise. We have experimented with a simple experimental environment, a method of predicting similarity to input data by adjusting the range of the input data or by changing the neural network structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.165-173
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• 2008

Hybrid ad hoc networks are integrated networks referred to Home Networks, Telematics and Sensor networks can offer various services. Specially, in ad hoc network where each node is responsible for forwarding neighbor nodes' data packets, it should net only reduce the overall energy consumption but also balance individual battery power. Unbalanced energy usage will result in earlier node failure in overloaded nodes. it leads to network partitioning and reduces network lifetime. Therefore, this paper studied the routing protocol considering efficiency of energy. The suggested algorithm can predict the status of energy in each node using the energy prediction model. This can reduce the overload of establishing route path and balance individual battery power. The suggested algorithm can reduce power consumption as well as increase network lifetime.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.55-62
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• 2009

The power consumption of a high-end microprocessor increases very rapidly. High power consumption will lead to a rapid increase in the chip temperature as well. If the temperature reaches beyond a certain level, chip operation becomes either slow or unreliable. Therefore various approaches for Dynamic Thermal Management (DTM) have been proposed. In this paper, we propose a learning based temperature prediction scheme for a multi-core system. In this approach, from repeatedly executing an application, we learn the thermal patterns of the chip, and we control the temperature in advance through DTM. When the predicted temperature may go beyond a threshold value, we reduce the temperature by decreasing the operation frequencies of the corresponding core. We implement our temperature prediction on an Intel's Quad-Core system which has integrated digital thermal sensors. A Dynamic Frequency System (DFS) technique is implemented to have four frequency steps on a Linux kernel. We carried out experiments using Phoronix Test Suite benchmarks for Linux. The peak temperature has been reduced by on average $5^{\circ}C{\sim}7^{\circ}C$. The overall average temperature reduced from $72^{\circ}C$ to $65^{\circ}C$.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.155-160
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• 2019

In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.