Short-term wind power prediction based on combined grey-Markov model

Chen, Sheng; Yang, Lin; Zhang, Gengwu; Zeng, Cheng; Dong, Shijun; Dai, Chao

doi:10.1109/apap.2011.6180647

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…In this methodology, the data present in the system error is extracted to ensure greater accuracy of forecasting. To establish enhanced accuracy of the forecast, a combined grey and Markov model is implemented to ensure greater precision [118], [119]. In this combined methodology, the errors forecasted by the grey model are categorized into different conditions using the Markov model and for each conditions the probability is estimated.…”

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

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“…The Grey Model GM(1,1) : The GM (1,1) is the most used in prediction systems [11]. The GM(1,1) modelling process :…”

Section: Machine Learning and Grey Model Descriptionmentioning

confidence: 99%

Spectrum Sensing Improvement in Cognitive Radio Networks for Real-Time Patients Monitoring

Ouattara

Krief

Chalouf

et al. 2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Regular monitoring of vital signs guarantees a preventive treatment of common diseases ensuring better health for people. Most of the proposed solutions in e-health context are based on a set of heterogeneous wireless sensors, fitting the patient and his environment. Often, these sensors are connected to a local smart node acting as a gateway to the outside (contacts, servers). When the patient is mobile, one of the issues we may face is the guarantee of a permanent connectivity between local smart node and the outside. To overcome this problem, we need to define a robust communications architecture able to benefit from different technologies and standards. This provides equipments with the ability to dispose of free-bands to perform their transmission anytime and anywhere. Cognitive radio, although appropriate technology, requires taking into account the interdependence between the patient's mobility and frequency band changes. Our proposal, is an anticipation model, a decision-making function that predicts the state of frequency bands occupancy. The model combines the machine learning techniques to the Grey Model system to provide low cost algorithm for spectral prediction which facilitates or guarantees permanent connectivity.

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“…Thus, the common way for high accuracy is to increase the complexity of the model. With the continuous development of artificial intelligence technology, wind power prediction based on machine learning [5][6][7][8] and deep learning [9][10][11][12][13] has become a hot topic, compared to the traditional physical and statistical methods, and the prediction accuracy has been greatly improved. Due to the complexity of the structures, the models have the ability to process a large number of data and to better reflect the non-linear and non-stationary nature of wind power.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complexity of the structures, the models have the ability to process a large number of data and to better reflect the non-linear and non-stationary nature of wind power. The work in Reference [5] used the Markov model divided residual error into several different states, the residual values were further forecasted through calculating the probability distribution of each state, the residual errors were used to correct the predicted values to improve the accuracy of wind speeds, and the results showed that the Markov model can improve the short-term forecasting accuracy of wind power effectively. The work in Reference [6] used the K-nearest neighbor (KNN) algorithm to select historical wind speed points and built an SVR model to obtain the fusion wind speed, which improves the accuracy of the wind speed prediction.…”

Section: Introductionmentioning

confidence: 99%

Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Yang

Zha

et al. 2020

Machines

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With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

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Short-term wind power prediction based on combined grey-Markov model

Cited by 18 publications

References 17 publications

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

Spectrum Sensing Improvement in Cognitive Radio Networks for Real-Time Patients Monitoring

Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

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