Analysis of wind power generation and prediction using ANN: A case study

Mabel, M. Carolin; Fernandez, Eugene

doi:10.1016/j.renene.2007.06.013

Cited by 292 publications

(29 citation statements)

References 13 publications

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“…They preferred three input variables such as wind speed, relative humidity, and generation hours and one output variable such as energy output for ANN. The results of this study showed that the proposed ANN model has high accuracy [10]. Kusiak et al studied on short-horizon prediction of wind power via a multilayer ANN with a single hidden layer [11].…”

Section: Introductionmentioning

confidence: 69%

Estimating the energy production of the wind turbine using artificial neural network

Mert

Karakuş

Üneş

2015

Neural Comput & Applic

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Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (P min ) and maximum (P max ) power generation values as well as the total power generation (P sum ) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, P min , P max , and P sum , were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models.Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phase of study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.

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Section: Introductionmentioning

confidence: 69%

Estimating the energy production of the wind turbine using artificial neural network

Mert

Karakuş

Üneş

2015

Neural Comput & Applic

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“…For example, Gan and Ke [34] used least square support vector machine (LSSVM) to predict wind power ramps. The most widely used method is the multilayer-perceptron (MLP) neural network [18,19,[35][36][37][38]. Kusiak et al [19] used five different algorithms to forecast wind power: support vector machine regression (SVM-R), an MLP neural network, a radial basis function (RBF) network, a classification and regression tree, and a random forest.…”

Section: State-of-the-art Methods (Soa)mentioning

confidence: 99%

“…Improvements in the prediction of wind power and ramps can also be achieved using other meteorological variables, in addition to wind speed [18,19]. Kamath [20,21] used a feature selection technique to evaluate the influence of several meteorological variables on the identification of ramp occurrences.…”

Section: Related Workmentioning

confidence: 99%

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Musilek

Lozowski

2017

Int J Data Sci Anal

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Much recent research in wind power forecasting has been focused on predicting large, sudden changes in wind power output, called wind ramps. State-of-the-art wind ramp prediction methods estimate future wind ramps from forecast power time series. We suggest that, by analyzing the weather associated with wind ramps, their forecasting can be improved. In particular, we propose a new method for wind ramp forecasting based on the analysis of forecast atmospheric pressure fields. Feature vectors relating to the pressure gradient are extracted from the pressure fields using an image texture extraction technique, called Gabor filtering. Numerical experiments show that these Gabor feature vectors are well correlated with power generation. They are used as inputs to a new wind power forecasting model. Compared with a basic state-of-the-art wind power forecasting model that does not use Gabor features as input, the proposed model exhibits better performance for power prediction, for two of the three wind farms chosen for this study. However, the ability of the model to forecast actual wind ramps is worse than that of the basic model, as measured by ramp capture rate and forecast accuracy. We also describe a second method to pre- Earth and Atmospheric Sciences, University of Alberta, Edmonton, Canada dict the magnitude of a sudden power change (wind ramp), using several input variables, in addition to Gabor features. Numerical experiments show that this second approach has better performance than the basic model, with respect to ramp capture rate and forecast accuracy. We suggest that it could be used operationally to supplement a current state-of-the-art ramp prediction model. We present an example of using this approach to provide a warning of a potential ramp.

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“…Cai et al [19] used the Long-range Energy Alternatives Planning System (LEAP) modelto predict and analyze the future power supply structure of China, and concluded that replacing coal-fired power plants with more efficient nuclear power is the future development trend in China. At present, there are three kinds of models commonly used in research: (1) Systematic models such as the System Dynamics(SD) model, gray system prediction model, and the Artificial Neural Network(ANN) model [20]; (2) Inheritance structure models, such as the LEAP model; (3) Hybrid class models. The System dynamics(SD) model was first proposed by the American scholar Forrester in the early 1950s [21], and it can solve problems of a complex system with high order and nonlinear multiple feedback, so it has been widely used in the energy research field [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the Evolution Mechanism and Development Forecasting of China’s Power Supply Structure Clean Development

Zhang

Yun

et al. 2017

Sustainability

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Abstract:The clean development of China's power supply structure has become a crucial strategic problem for the low-carbon, green development of Chinese society. Considering the subsistent developments of optimized allocation of energy resources and efficient utilization, the urgent need to solve environmental pollution, and the continuously promoted power market-oriented reform, further study of China's power structure clean development has certain theoretical value. Based on the data analysis, this paper analyzes the key factors that influence the evolution process of the structure with the help of system dynamics theory and carries out comprehensive assessments after the construction of the structure evaluation system. Additionally, a forecasting model of the power supply structure development based on the Vector Autoregressive Model (VAR) has been put forward to forecast the future structure. Through the research of policy review and scenario analysis, the paths and directions of structure optimization are proposed. In this paper, the system dynamics, vector autoregressive model (VAR), policy mining, and scenario analysis methods are combined to systematically demonstrate the evolution of China's power structure, and predict the future direction of development. This research may provide a methodological and practical reference for the analysis of China's power supply structure optimization development and for theoretical studies.

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Analysis of wind power generation and prediction using ANN: A case study

Cited by 292 publications

References 13 publications

Estimating the energy production of the wind turbine using artificial neural network

Estimating the energy production of the wind turbine using artificial neural network

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Study on the Evolution Mechanism and Development Forecasting of China’s Power Supply Structure Clean Development

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