Short-Term Wind Speed and Output Power Forecasting Based on WT and LSSVM

2009 ◽  
Author(s):  
Dejun Liu ◽  
Hui Li
Keyword(s):  
Wind Speed ◽  
Output Power ◽  
Short Term ◽  
Power Forecasting

scholarly journals Deep Learning for Wave Energy Converter Modeling Using Long Short Term Memory

2021 ◽  
Author(s):  
Seyed Milad Mousavi ◽  
Majid Ghasemi ◽  
Mahsa Dehghan Manshadi ◽  
Amir Mosavi
Keyword(s):  
Wind Speed ◽  
Output Power ◽  
Wave Energy ◽  
Short Term Memory ◽  
Ocean Waves ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

scholarly journals Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

scholarly journals Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

2017 ◽  
Vol 167 ◽  
pp. 395-405 ◽  
Author(s):  
Monowar Hossain ◽  
Saad Mekhilef ◽  
Malihe Danesh ◽  
Lanre Olatomiwa ◽  
Shahaboddin Shamshirband
Keyword(s):  
Extreme Learning Machine ◽  
Output Power ◽  
Short Term ◽  
Pv Systems ◽  
Learning Machine ◽  
Power Forecasting

Short-Term Power Forecasting of Small-Scale Hydropower Based on Projection Pursuit Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 764-769
Author(s):  
Wen Xia Liu ◽  
Xi Zhou ◽  
Xiao Bo Xu ◽  
Mei Mei Xu
Keyword(s):  
Output Power ◽  
Load Forecasting ◽  
Projection Pursuit ◽  
Forecast Accuracy ◽  
Small Scale ◽  
Forecasting Model ◽  
Practical Application ◽  
Short Term ◽  
Forecasting Method ◽  
Power Forecasting

Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great difficult to the operation of the power system. Most of the existing small-scale hydropower forecasting is considered as the load forecasting factors, and there is not effective forecasting method. This paper establishes an output power forecasting model of the small-scale hydropower based on Projection Pursuit. The simulation results show that the new algorithm has a strong practical application in the small-scale hydropower output power forecasting and the forecast accuracy meets the scheduling requirements.

Short-term wind speed and power forecasting using an ensemble of mixture density neural networks

2016 ◽  
Vol 87 ◽  
pp. 203-211 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Deyong Wen ◽  
Yongsheng Chen
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Short Term ◽  
Mixture Density ◽  
Power Forecasting

scholarly journals A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4815 ◽  
Author(s):  
Yosui Miyazaki ◽  
Yusuke Kameda ◽  
Junji Kondoh
Keyword(s):  
Power Systems ◽  
Output Power ◽  
Power Output ◽  
Percentage Error ◽  
Support Vector ◽  
Short Term ◽  
Pv System ◽  
Forecasting Method ◽  
Geographically Distributed ◽  
Power Forecasting

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

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