Short-term wind power forecasting based on lifting wavelet transform and SVM

Wen, Jinbin; Wang, Xin; Zheng, Yongjun; Li, Lixue; Zhou, Lei; Yao, Gang; Chen, Hongtao

doi:10.1109/peam.2012.6612530

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…The lowest MAPE obtained was 2.5%. In [129], lifting wavelet transform and Support Vector Machine (SVM) are used for model building. Wavelet transform characterizes the original wind speed and SVM improves the prediction accuracy.…”

Section: Review Of Various Machine Learning Methods For Wind Forecastmentioning

confidence: 99%

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Prema¹,

Bhaskar

Almakhles

et al. 2022

IEEE Access

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Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

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Section: Review Of Various Machine Learning Methods For Wind Forecastmentioning

confidence: 99%

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Prema¹,

Bhaskar

Almakhles

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Another way is to establish a high-accuracy three-dimensional atmospheric physical model and calculate the wind speed and wind direction of each wind turbine based on the boundary conditions of the wind farm, which can finally be transferred to wind power of wind farm [5]. The main methods of statistical prediction are time series method [6], neural network [7], Support Vector Machine (SVM) [8], etc. The time series method establishes a time series model based on a large number of historical data and forecasts wind power with the time series model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

et al. 2016

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Abstract. Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.

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“…Physical forecasting model established mainly based on NWP (Numerical Weather Prediction) [3], transferring the wind speed and the wind direction of NWP into those of Turbine hub height, helps forecasting the wind power of each wind turbine [4], [5]. The main methods of statistical prediction commonly contains several methods, such as time series method, improved time series method [6], Feedforward Neural Network (FNN) [7], Regression Neural Network, Back propagation Neural Network (BP) [8], Support Vector Machine (SVM) [9], grey theory, genetic algorithm and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

et al. 2016

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Abstract. Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

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Short-term wind power forecasting based on lifting wavelet transform and SVM

Cited by 7 publications

References 1 publication

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

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