• Title, Summary, Keyword: prediction

Search Result 21,083, Processing Time 0.072 seconds

Wind Speed Prediction using WAsP for Complex Terrain (WAsP을 이용한 복잡지형의 풍속 예측 및 보정)

  • Yoon, Kwang-Yong;Paek, In-Su;Yoo, Neung-Soo
    • 한국신재생에너지학회:학술대회논문집
    • /
    • /
    • pp.268-273
    • /
    • 2008
  • A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

  • PDF

Wind Speed Prediction using WAsP for Complex Terrain (복합지형에 대한 WAsP의 풍속 예측성 평가)

  • Yoon, Kwang-Yong;Yoo, Neung-Soo;Paek, In-Su
    • Journal of Industrial Technology
    • /
    • v.28 no.B
    • /
    • pp.199-207
    • /
    • 2008
  • A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

  • PDF

A Climate Prediction Method Based on EMD and Ensemble Prediction Technique

  • Bi, Shuoben;Bi, Shengjie;Chen, Xuan;Ji, Han;Lu, Ying
    • Asia-Pacific Journal of Atmospheric Sciences
    • /
    • v.54 no.4
    • /
    • pp.611-622
    • /
    • 2018
  • Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea

  • Kim, WonMoo;Yeo, Sae-Rim;Kim, Yoojin
    • Asia-Pacific Journal of Atmospheric Sciences
    • /
    • v.54 no.4
    • /
    • pp.563-573
    • /
    • 2018
  • An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

Complexity Reduction of Intra Prediction in H.264/AVC (H.264/AVC를 위한 효율적인 인트라 예측 기법)

  • 이남숙;이재헌
    • Proceedings of the IEEK Conference
    • /
    • /
    • pp.125-128
    • /
    • 2003
  • In this paper, we propose two methods for complexity reduction of intra prediction in H.264/AVC. One is skipping of intra prediction using inter prediction cost at current macroblock in current P picture, average of intra prediction cost in previous I picture, and average of inter prediction cost in previous P picture. The other is skipping of intra 16$\times$16 prediction using intra 4$\times$4 prediction cost and modes. As a result, complexity of intra prediction in P picture and that of intra 16$\times$16 prediction in intra prediction macroblock can be reduced by about 80~99% and 50~93%, respectively.

  • PDF

Short-term Wind Power Prediction Based on Empirical Mode Decomposition and Improved Extreme Learning Machine

  • Tian, Zhongda;Ren, Yi;Wang, Gang
    • Journal of Electrical Engineering and Technology
    • /
    • v.13 no.5
    • /
    • pp.1841-1851
    • /
    • 2018
  • For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

Prediction Accuracy Evaluation of Domain and Domain Combination Based Prediction Methods for Protein-Protein Interaction

  • Han, Dong-Soo;Jang, Woo-Hyuk
    • Bioinformatics and Biosystems
    • /
    • v.1 no.2
    • /
    • pp.128-133
    • /
    • 2006
  • This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

  • PDF

Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids

  • Le, Yiwen;He, Jinghan
    • Journal of Electrical Engineering and Technology
    • /
    • v.12 no.3
    • /
    • pp.1053-1063
    • /
    • 2017
  • Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

GOP ARIMA based Bandwidth Prediction for Non-stationary VBR Traffic (MPEG VBR 트래픽을 위한 GOP ARIMA 기반 대역폭 예측기법)

  • Kang, Sung-Joo;Won, You-Jip
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.301-303
    • /
    • 2004
  • In this work, we develop on-line traffic prediction algorithm for real-time VBR traffic. There are a number of important issues: (i) The traffic prediction algorithm should exploit the stochastic characteristics of the underlying traffic and (ii) it should quickly adapt to structural changes in underlying traffic. GOP ARIMA model effectively addresses this issues and it is used as basis in our bandwidth prediction. Our prediction model deploy Kalman filter to incorporate the prediction error for the next prediction round. We examine the performance of GOP ARIMA based prediction with linear prediction with LMS and double exponential smoothing. The proposed prediction algorithm exhibits superior performam againt the rest.

  • PDF

A method for intra-prediction in the Integer DCT domain of H.264 (H.264의 integer DCT 영역에서의 Intra-prediction 기법)

  • Ahn, Hyeong-Jin;Oh, Hyung-Suk;Kim, Won-Ha
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.91-92
    • /
    • 2008
  • 본 논문에서는 기존의 H.264/AVC의 spatial 영역에서 Intra prediction 기법과 달리 H.264/AVC에서 사용하는 Integer DCT 영역에서 Intra prediction 기법을 제안한다. 이를 위하여 Integer DCT 영역에서 Intra prediction을 수행하는 모든 과정을 matrix multiplication으로 표현하여 Intra prediction을 수행하는 matrix를 유도한다. Intra prediction을 수행하는 matrix를 각 모드에 알맞게 설계하고, 이 matrix를 Integer DCT 영역에서 사용할 수 있도록 orthogonal한 Integer matrix를 설계한다. 실험을 통하여 제안한 Integer DCT 영역에서 Intra prediction 기법이 기존의 H.264/AVC의 spatial 영역에서 intra prediction 기법과 성능이 동일하면서 어떻게 matrix multiplication에 연산들을 포함시켜서 단순화 할 수 있는지를 보여주겠다. 또한 H.264/AVC에서 제공하는 intra prediction 각 모드에 대해 계산상 복잡도를 분석하였다.

  • PDF