Forecasting renewable energy for environmental resilience through computational intelligence

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

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Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽

10.3390/pr7110843 ◽

2019 ◽

Vol 7 (11) ◽

pp. 843 ◽

Cited By ~ 5

Author(s):

Keke Wang ◽

Dongxiao Niu ◽

Lijie Sun ◽

Hao Zhen ◽

Jian Liu ◽

...

Keyword(s):

Wind Power ◽

Extreme Learning Machine ◽

Harmony Search ◽

Forecasting Model ◽

Short Term ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Correlation Degree ◽

Learning Machine ◽

Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

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Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.1909 ◽

2013 ◽

Vol 860-863 ◽

pp. 1909-1913

Author(s):

Hai Xiang Xu ◽

Peng Wang ◽

Xiao Meng Ren

Keyword(s):

Energy Storage ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Power Station ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Assessment Standard ◽

The Impact ◽

Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

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Offshore Wind Turbines Operation and Maintenance in China: A Case Study of Donghai Bridge Offshore Wind Farm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.1871 ◽

2013 ◽

Vol 448-453 ◽

pp. 1871-1874

Author(s):

Yuan Xie

Keyword(s):

Wind Power ◽

Wind Turbines ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Power ◽

Offshore Wind Turbines ◽

Operation And Maintenance ◽

Steady Stage ◽

Power Project

China has great potential in offshore wind energy and makes an ambitious target for offshore wind power development. Operation and Maintenance (O&M) of offshore wind turbines become more and more important for China wind industry. This study introduces the current offshore wind power projects in China. Donghai Bridge Offshore Demonstration Wind Farm (Donghai Bridge Project) is the first commercial offshore wind power project in China, which was connected to grid in June 2010. O&M of Donghai Bridge Project represent the state-of-the-art of China offshore O&M. During the past two and half years, O&M of Donghai Bridge Project has gone through three phases and stepped into a steady stage. Its believed that analysis of O&M of Donghai Bridge Project is very helpful for Chinas offshore wind power in the future.

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Wind Power Forecasting Model Fusion Evaluation Based on Comprehensive Weights

Volume 6B: Energy ◽

10.1115/imece2016-65668 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jianyan Tian ◽

Tingting Liu ◽

Amit Banerjee ◽

Aixue Wei ◽

Shengqiang Yang ◽

...

Keyword(s):

Wind Power ◽

Evaluation Method ◽

Forecasting Model ◽

Model Optimization ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Single Index ◽

Forecasting Models ◽

The Individual ◽

Power Forecasting

Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.

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Floating offshore wind turbines - technology and potential

Analele Universităţii "Dunărea de Jos" din Galaţi. Fascicula XI, Construcţii navale/ Annals of "Dunărea de Jos" of Galati, Fascicle XI, Shipbuilding ◽

10.35219/annugalshipbuilding.2020.43.11 ◽

2020 ◽

Vol 43 ◽

pp. 89-94

Author(s):

Aurel Dan Maimon

Keyword(s):

Wind Power ◽

Wind Turbines ◽

Production Efficiency ◽

Short Review ◽

Offshore Wind ◽

Offshore Wind Power ◽

Offshore Wind Turbines ◽

Floating Offshore Wind Turbines ◽

Visual Impact ◽

The One

"The main purpose of this paper is to present a short review of the actual progress on the floating offshore wind turbines. Floating offshore wind turbines have several advantages: overcoming the depth constraint, floating offshore wind turbines can be installed further offshore and therefore on the one hand have little or no visual impact from the coast, and on the other hand to take advantage of more constant and stronger winds, thus increasing the production efficiency of electricity. They are assembled to port and then transported to site with an ordinary tug, which can also bring them ashore for heavy maintenance or final dismantling. Floating wind power is the future of offshore wind power."

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“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

Mathematical Problems in Engineering ◽

10.1155/2015/897952 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10

Author(s):

Dunnan Liu ◽

Yu Hu ◽

Yujie Xu ◽

Canbing Li

Keyword(s):

Wind Speed ◽

Wind Power ◽

Correction Method ◽

Relational Model ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Wind Speed Forecasting ◽

The Stability ◽

Cloud Theory ◽

Power Forecasting

As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

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