scholarly journals An Ensemble Model of Wind Speed Forecasting Based on Variational Mode Decomposition and Bare-Bones Fireworks Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jicheng Quan ◽  
Li Shang
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Feature Reduction ◽  
Partial Least Square ◽  
Ensemble Prediction ◽  
Wind Speed Forecasting ◽  
Proposed Model

Wind energy is one of the fastest growing renewable energy sources. Wind speed forecasting is essential to enhance the utilization of wind energy. Various prediction models have been developed to improve the prediction accuracy of wind speed. However, wind speed time series has nonlinearity, fluctuation, and intermittence, which makes the prediction difficult. Existing prediction models ignore data decomposition and feature reduction and suffer from the deficiency of individual models. This paper proposes a novel ensemble prediction model, which integrates data preprocessing, feature selection, parameter optimization, three intelligent prediction models, and an ensemble strategy. To improve prediction performance, a highly efficient optimization algorithm is applied to determine the individual models’ optimal parameters. Furthermore, partial least square regression is used to calculate combination weight. Additionally, two 10 min datasets from the National Renewable Energy Laboratory (NREL) are employed for one-step-ahead prediction. Among the involved models, the proposed model can obtain the best prediction accuracy. The experimental results indicate that the mean absolute percent errors of the proposed model are 7.97% and 9.99%, which are lower than the comparison methods. Pearson’s test reveals that the proposed approach can have the strongest association between the actual data and the prediction results.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

Application of the Maximum Entropy Method for Determining a Sensitive Distribution in the Renewable Energy Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Gholamhossein Yari ◽  
Zahra Amini Farsani
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Maximum Entropy ◽  
Energy Systems ◽  
Rayleigh Distribution ◽  
Entropy Method ◽  
Climatic Data ◽  
Data Set ◽  
Renewable Energy Systems

In the field of the wind energy conversion, a precise determination of the probability distribution of wind speed guarantees an efficient use of the wind energy and enhances the position of wind energy against other forms of energy. The present study thus proposes utilizing an accurate numerical-probabilistic algorithm which is the combination of the Newton’s technique and the maximum entropy (ME) method to determine an important distribution in the renewable energy systems, namely the hyper Rayleigh distribution (HRD) which belongs to the family of Weibull distribution. The HRD is mainly used to model the wind speed and the variations of the solar irradiance level with a negligible error. The purpose of this research is to find the unique solution to an optimization problem which occurs when maximizing Shannon’s entropy. To confirm the accuracy and efficiency of our algorithm, we used the long-term data for the average daily wind speed in Toyokawa for 12 yr to examine the Rayleigh distribution (RD). This data set was obtained from the National Climatic Data Center (NCDC) in Japan. It seems that the RD is more closely fitted to the data. In addition, we presented different simulation studies to check the reliability of the proposed algorithm.

Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method

2009 ◽  
Author(s):  
Gong Li ◽  
Jing Shi ◽  
Junyi Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term ◽  
Goodness Of Fit Test ◽  
Wind Speed Forecasting

Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.

Study on Wind Speed Forecasting Based on STC and BP Neural Network

2013 ◽  
Vol 724-725 ◽  
pp. 623-629
Author(s):  
Xing Jie Liu ◽  
Wen Shu Zheng ◽  
Tian Yun Cen
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Target Prediction ◽  
Temporal Correlation ◽  
Economic Security ◽  
Wind Speed Forecasting ◽  
Spatio Temporal

Accurate wind speed forecasting of wind farm is of great significance in economic security and stability of the grid. In order to improve the prediction accuracy, the paper first proposed a spatio-temporal correlation predictor method. Based on physical characteristics of wind speed evolution, the method looked for the wind speed and direction information at sites close to the target prediction site, and established STCP model to forecast. And then we established the BP neural network to finish multi-step forecast with wind speed time series of target forecast site .Last, two methods were combined to form STCP-BP method. Simulation tests are conducted with operation data from certain wind farm group in China and results show that STCP-BP method can effectively improve the prediction accuracy compared with BP model.

Forecasting the Wind to Reach Significant Penetration Levels of Wind Energy

2011 ◽  
Vol 92 (9) ◽  
pp. 1159-1171 ◽  
Author(s):  
Melinda Marquis ◽  
Jim Wilczak ◽  
Mark Ahlstrom ◽  
Justin Sharp ◽  
Andrew Stern ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Science ◽  
Time Frame ◽  
Department Of Energy ◽  
Power Prediction ◽  
Financial Benefit ◽  
The Government

Advances in atmospheric science are critical to increased deployment of variable renewable energy (VRE) sources. For VRE sources, such as wind and solar, to reach high penetration levels in the nation's electric grid, electric system operators and VRE operators need better atmospheric observations, models, and forecasts. Improved meteorological observations through a deep layer of the atmosphere are needed for assimilation into numerical weather prediction (NWP) models. The need for improved operational NWP forecasts that can be used as inputs to power prediction models in the 0–36-h time frame is particularly urgent and more accurate predictions of rapid changes in VRE generation (ramp events) in the very short range (0–6 h) are crucial. We describe several recent studies that investigate the feasibility of generating 20% or more of the nation's electricity from weather-dependent VRE. Next, we describe key advances in atmospheric science needed for effective development of wind energy and approaches to achieving these improvements. The financial benefit to the nation of improved wind forecasts is potentially in the billions of dollars per year. Obtaining the necessary meteorological and climatological observations and predictions is a major undertaking, requiring collaboration from the government, private, and academic sectors. We describe a field project that will begin in 2011 to improve short-term wind forecasts, which demonstrates such a collaboration, and which falls under a recent memorandum of understanding between the Office of Energy Efficiency and Renewable Energy at the Department of Energy and the Department of Commerce/National Oceanic and Atmospheric Administration.

scholarly journals ANALISIS KELAYAKAN TURBIN ANGIN KECEPATAN RENDAH TIPE NT1000W DI WILAYAH TERPENCIL

2019 ◽  
Vol 10 (1) ◽  
pp. 84-93
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Electrical Energy ◽  
Remote Communities ◽  
Low Speed ◽  
Energy Needs ◽  
One Step ◽  
Research Studies

This research discusses the fulfillment of the electricity needs of remote communities that are closely related to electrification ratios. Electrification ratios in some isolated areas and scattered islands in Indonesia are still very low. To date, most of the electricity needs in Indonesia is still supplied by Diesel Power Electricity Generator (PLTD) which uses diesel as its fuel. Therefore, it is necessary the utilization of renewable energy as one step to fulfill the electrical energy needs. This research studies about the utilization of wind energy with PLTB by using low speed wind turbine to fulfill the electricity needs of remote communities and scattered islands in Indonesia. NT1000W is the latest technology of low speed wind turbine that can operate at wind speed of 1 m/d up to 60 m/d appropriate to the wind conditions in Indonesia. Testing conducted in west Sumatera particularly in Padang city and Kapo-Kapo Island provide a feasibility of PLTB NT1000W technically and financially.

scholarly journals A Techno-Economic Model for Wind Energy Costs Analysis for Low Wind Speed Areas

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1463 ◽  
Author(s):  
Kehinde A. Adeyeye ◽  
Nelson Ijumba ◽  
Jonathan S. Colton
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Economic Model ◽  
African Continent ◽  
Financial Return ◽  
Levelized Cost Of Electricity ◽  
Low Wind Speed ◽  
Ferris Wheel

The global population is moving away from fossil fuel technologies due to their many disadvantages, such as air pollution, greenhouse gases emission, global warming, acid rain, health problems, and high costs. These disadvantages make fossil fuels unsustainable. As a result, renewable energy is becoming more attractive due to its steadily decreasing costs. Harnessing renewable energy promises to meet the present energy demands of the African continent. The enormous renewable energy potential available across the African continent remains largely untapped, especially for wind energy. However, marginal and fair wind speeds and power densities characterize African wind energy resulting in low and unsustainable power in many areas. This research develops a techno-economic model for wind energy cost analysis for a novel, Ferris wheel-based wind turbine. The model is used to techno-economically analyze the siting of wind turbine sites in low wind speed areas on the African continent. The wind turbine’s technical performance is characterized by calculating the annual energy production and the capacity factor using the wind Weibull probability distribution of the cities and theoretical power curve of the wind turbine. Its economic performance is evaluated using annualized financial return on investment, simple payback period, and levelized cost of electricity. The techno-economic model is validated for 21 African cities and shows that the Ferris wheel-based design is very competitive with four current, commercial wind turbines, as well as with other sources of energy. Hence, the new wind turbine may help provide the economical, clean, renewable energy that Africa needs.

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