• Journal of the Korea Society of Computer and Information
• /
• v.17 no.1
• /
• pp.211-218
• /
• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• Journal of Energy Engineering
• /
• v.28 no.4
• /
• pp.82-93
• /
• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Journal of Navigation and Port Research
• /
• v.43 no.6
• /
• pp.335-343
• /
• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• 한국신재생에너지학회:학술대회논문집
• /
• 2011.11a
• /
• pp.73.1-73.1
• /
• 2011

분류층 석탄가스화기에서 슬래그의 원활한 배출은 가스화 플랜트 운전 및 성능에 중대한 영향을 미치는 것으로 알려져 있다. 가스화기의 운전 온도에서 슬래그 점도가 일정수준 이상인 경우에는 가스화기 하부 슬래그 배출구 막힘 현상을, 일정 수준 이하일 경우에는 Membrane wall의 slag 두께가 얇아져 가스화기 수냉벽에 열적 악영향을 미친다. 가스화기의 안정적인 운전을 위한 석탄 선정 시, 석탄 슬래그의 용융온도 및 점도의 파악이 중요하다. 일반적으로 석탄슬래그의 용융온도는 ASTM D-1857 절차에 따른 환원분위기에서의 회융유온도(FT)측정을 통해, 점도는 고온점도측정 실험을 통해 분석하고 있다. 이런 실험적인 분석방법은 다양한 슬래그조성 및 온도 변화에 따른 영향을 살펴보기에는 많은 시간과 비용이 발생하므로 슬래그조성 및 온도 변화에 따른 용융온도 및 점도 예측이 필요하다. 본 연구에서는 200여 탄종의 회용유점 측정 결과와 FactSage에서 예측되는 슬래그 결정상 생성 및 회용유점(FT)에서의 고체분율과의 상관관계를 분석하였다. 이를 바탕으로 다양한 Ash 조성(SiO2, Al2O3, Fe2O3, CaO)에 대한 회용유점(FT)을 예측할 수 있는 프로그램을 개발하였다. 또한 50여 탄종의 슬래그 점도 측정 결과를 Facsage에서 예측되는 결정상 종류 및 Ash 조성을 기준으로 분류하였다. 결정상 종류 및 Ash 조성을 기준으로 기존 슬래그점도예측모델를 활용하여 보다 정확한 슬래그 점도 예측 프로세스를 개발하였다. 본 연구 결과는 플랜트 운전 결과 검증을 통하여 석탄 가스화 플랜트에 적합한 석탄의 선정, 혼탄 비율 및 첨가제 투입량 결정을 위해 활용될 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.1
• /
• pp.169-178
• /
• 2019

Now a day, we can perform various predictions by applying machine learning, which is a field of artificial intelligence; however, the finding of best algorithm in the field is always the problem. This paper predicts monthly power trading amount, monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive using machine learning supervised algorithms. Then, we find most fit algorithm among them for each case. To do this we show the probability of predicting the value for monthly power trading amount and monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive. Then, we try to average each predicting values. Finally, we confirm which algorithm is the most superior algorithm among them.

• Tunnel and Underground Space
• /
• v.34 no.2
• /
• pp.143-153
• /
• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.6
• /
• pp.48-53
• /
• 1995

A considerable number of methods are available for predicting traffic noise levels of road networks where sound is freely propagating. But surrounding buildings reflect back sound to the road and sound energy is increased by these reflectors. Therefore, this study was focussed on the establishment of the prediction method of road traffic noise under reflective conditions. This prediction method was developed by establishing prediction formulas of noise level such as $L_{10},\;L_{50},\;L_{90}\;and\;L_{eq}$. The sound energy density was employed to establish prediction formulas in terms of independent variables. The validity of the proposed prediction formulas was been confirmed by applying them to actually measured parameters of road traffic noise and noise level data. On the whole, the agreement between measured and predicted noise levels appeared to be satisfactory. The conclusion might be reached that the method developed in this study could be used to predict road traffic noise under reflective conditions.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.3
• /
• pp.19-27
• /
• 2015

Technologies of wind power generation for development of alternative energy technology have been accumulated over the past 20 years. Wind power generation is environmentally friendly and economical because it uses the wind blowing in nature as energy resource. In order to operate wind power generation efficiently, it is necessary to accurately predict wind speed changing every moment in nature. It is important not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan, minimizing the largest absolute error plays an important role for building flexible generation operating plan because the difference between predicting power and real power causes economic loss. In this paper, we propose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecast model made to analyze the wind speed forecast given by the Meteorological Administration and pattern value for considering seasonal property of wind speed as well as changing trend of past wind speed. The wind speed forecast given by the Meteorological Administration is the forecast in respect to comparatively wide area including wind generation farm. But it contributes considerably to make accuracy of wind speed prediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in more detail, the greater accuracy will be obtained.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.11
• /
• pp.2569-2574
• /
• 2010

In this paper, we introduce an efficient energy management using a notion of virtual energy system for shared solar-powered sensor network. Virtual energy system is an abstraction that allows sensor network applications on a node to reserve their own fractions of the shared solar cell and the shared rechargeable battery, hence achieving logically partition of a shared renewable power source. Our results show that our design and implementation are reliable, lightweight and efficient, allowing proper isolation of energy consumption among applications.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.11
• /
• pp.527-536
• /
• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.