scholarly journals A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chao Yuan ◽  
Yiming Tang ◽  
Rui Mei ◽  
Fei Mo ◽  
Hong Wang
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Time Scale ◽  
Forecast Model ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Speed Prediction ◽  
Wind Power Forecast ◽  
Speed Prediction ◽  
Power Sequence

To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Impact of offshore wind power forecast error in a carbon constraint electricity market

Energy ◽  
2014 ◽  
Vol 76 ◽  
pp. 187-197 ◽  
Author(s):  
P. Higgins ◽  
A.M. Foley ◽  
R. Douglas ◽  
K. Li
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Forecast Error ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Power Forecast

scholarly journals Machine Intelligent Hybrid Methods Based on Kalman Filter and Wavelet Transform for Short-Term Wind Speed Prediction

Wind ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 37-50
Author(s):  
Yug Patel ◽  
Dipankar Deb
Keyword(s):  
Kalman Filter ◽  
Wavelet Transform ◽  
Wind Speed ◽  
Hybrid Methods ◽  
Moving Average ◽  
Offshore Wind ◽  
Electricity Grid ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Hybrid Forecasting

Wind power’s increasing penetration into the electricity grid poses several challenges for power system operators, primarily due to variability and unpredictability. Highly accurate wind predictions are needed to address this concern. Therefore, the performance of hybrid forecasting approaches combining autoregressive integrated moving average (ARIMA), machine learning models (SVR, RF), wavelet transform (WT), and Kalman filter (KF) techniques is essential to examine. Comparing the proposed hybrid methods with available state-of-the-art algorithms shows that the proposed approach provides more accurate prediction results. The best model is a hybrid of KF-WT-ML with an average R2 score of 0.99967 and RMSE of 0.03874, followed by ARIMA-WT-ML with an average R2 of 0.99796 and RMSE of 0.05863 over different datasets. Moreover, the KF-WT-ML model evaluated on different terrains, including offshore and hilly regions, reveals that the proposed KF based hybrid provides accurate wind speed forecasts for both onshore and offshore wind data.

Coordinated frequency control from offshore wind power plants connected to multi terminal DC system considering wind speed variation

2016 ◽  
Vol 11 (8) ◽  
pp. 1226-1236 ◽  
Author(s):  
Jayachandra N. Sakamuri ◽  
Müfit Altin ◽  
Anca D. Hansen ◽  
Nicolaos A. Cutululis
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Frequency Control ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Speed Variation ◽  
Dc System ◽  
Wind Power Plants

scholarly journals Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

2020 ◽  
Author(s):  
Saeed Samadianfard ◽  
Sajjad Hashemi ◽  
Katayoun Kargar ◽  
Mojtaba Izadyar ◽  
Ali Mostafaeipour ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Hybrid Model ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Multi Layer Perceptron ◽  
Wind Speed Prediction ◽  
Whale Optimization ◽  
Speed Prediction ◽  
Mlp Model

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

scholarly journals Wind speed prediction in northeastern Brazil municipalities through Linear Regression and Nonlinear for wind power generation purposes

2016 ◽  
Vol 9 (3) ◽  
pp. 927-939
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
Oseas Machado Gomes ◽  
João Bosco Verçosa leal Junior
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Linear Regression ◽  
Wind Power ◽  
Wind Power Generation ◽  
Northeastern Brazil ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Predição de velocidade do vento em regiões do nordeste brasileiro através de Regressão Linear e Não Linear para fins de geração eólica (Wind speed prediction in areas of northeastern Brazil through Linear Regression and Nonlinear for wind power...)

2016 ◽  
Vol 9 (3) ◽  
pp. 927
Author(s):  
Henrique Do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
Oseas Machado Gomes ◽  
João Bosco Verçosa Leal Junior
Keyword(s):  
Wind Speed ◽  
Linear Regression ◽  
Wind Power ◽  
Northeastern Brazil ◽  
Wind Speed Prediction ◽  
Speed Prediction

Esses trabalho utiliza modelos de Regressão Linear Simples (RLS) e Regressão Não Linear (RNL) para predição de médias mensais de velocidade do vento em regiões do nordeste brasileiro. Para isso foram utilizados dados de velocidade de vento ao nível de 10 m de altura no período de 2010 à 2014 como variável preditora. E como variável explicativa em ambos modelos foi utilizado o Deficit de Pressão de Vapor d'água no ar (DPV), o qual depende da temperatura do ar e da umidade do ar. Na configuração de modelos de RNL foram ajustados dois, um modelo exponencial crescente (RNL1) e um modelo quadrático (RNL2). Foram aplicados testes estatísticos como, Shapiro-Wilk, Durbin-Watson e Breusch-Pagan, todos sobre os resíduos dos modelos para averiguar a destreza dos mesmos. O menor valor da Média do Erro Absoluto Percentual (MAPE) foi encontrado em aproximadamente 12.94% para o modelo de RLS. Já para os modelo de RNL1 e RNL2, os valores encontrados foram 12,87% e 18.80%, respectivamente. O presente trabalho possui ainda como objetivo o incentivo de pesquisas de fontes de energias renováveis como é o caso do recurso eólico e consequentemente seu uso como alternativa energética para o avanço social e econômico do país.

scholarly journals A Hybrid Forecasting Method for Wind Speed

2018 ◽  
Vol 232 ◽  
pp. 03013
Author(s):  
Jian Jiao
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Power ◽  
Hybrid Method ◽  
Renewable Energy Sources ◽  
Arima Model ◽  
Wind Power Generation ◽  
Ann Model ◽  
Wind Speed Prediction ◽  
Speed Prediction

Wind energy is one of the most widely used renewable energy sources. Wind power generation is uncertain because of the intermittent of wind power. To reduce the influence of wind power generation on the power system, it is necessary to forecast wind speed. This paper presents a hybrid wind speed prediction method based on Autoregressive Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) model. In three wind speed prediction tests, the hybrid, ARIMA and ANN models are applied respectively. By analyzing the predicted results, it can be concluded that the hybrid method has better forecasting result. By analyzing the results, we can conclude that the hybrid method has better prediction effect.

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