A modified GM(1,1) model to accurately predict wind speed

Yousuf, Muhammad Uzair; Al‐Bahadly, Ibrahim; Avci, Ebubekir

doi:10.1016/j.seta.2020.100905

Cited by 25 publications

(11 citation statements)

References 46 publications

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“…Palmerston North (latitude: 40.382°S, longitude: 175.609°E, height: 21m) has a huge amount of strong winds (> 8.6 / ) with west-northwest as dominant wind direction [9,84]. From recorded data, the strong winds occurred 18% in autumn, 19% in winter, 26% in summer, and 37% in spring.…”

Section: A Data Set Descriptionmentioning

confidence: 99%

“…Both types of models show higher accuracy in very short to short-term forecasts. Commonly used statistical models include Kalman Filter [5], Markov Chain [6], Auto-Regressive Integrated Moving Average (ARIMA) [7], generalized additive model [8], and grey prediction models [9]. Similarly, the traditional AI/ML models include Artificial Neural Network (ANN) [10], Support Vector Regression (SVR) [11], and Fuzzy Logic (FL) [12].…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

2021

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Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

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Section: A Data Set Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

2021

Self Cite

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“…Given the problem that Numerical Weather Prediction models are not applicable to heterogeneous terrain, by combining WRF mesoscale model and HDWind microscale model, Prieto-Herráez et al [11] proposed a dynamic downscaling method to improve the local accuracy of wind speed prediction. However, the physical method is not suitable for short-term prediction because it requires a long time to run [12].…”

Section: Literature Review 121 Research Progress On Forecasting Wind Power Generationmentioning

confidence: 99%

“…Overall, a single symmetric equal-weight moving average filter with a specific number of terms can smooth out the periodic fluctuations of the corresponding frequency (the number of terms must be greater than or equal to the period length of the fluctuation). For seasonal fluctuations of quarterly (monthly) data with a period of 4 (12), we can eliminate seasonality well by using symmetrical equal-weight moving average filtering with an item number of 4 (12). However, when N=4 (12), the number of items is even, and an asymmetric equal-weight moving average filter cannot be constructed.…”

Section: Moving Average Filtering Algorithmmentioning

confidence: 99%

Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

Sui

Qian

2021

RAIRO-Oper. Res.

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Renewable energy represented by wind energy plays an increasingly important role in China's national energy system. The accurate prediction of wind power generation is of great significance to China's energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China's wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China's energy-saving and emission reduction targets.

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“…The GM (1,1) model has some advantages, such as being simple to calculate, analyze, fast to implement, and effective at predicting when enhanced. Therefore, the GM (1,1) model has been applied to different research applications, and it has demonstrated satisfactory prediction results when it is improved [17][18][19][20]. The GM (1,1) model is constructed using the system dataset that considers the model's input [21]; thus, the dataset needs to be free of negatives, randoms, and anomalies.…”

Section: Introductionmentioning

confidence: 99%

Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model

2021

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This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used.

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A modified GM(1,1) model to accurately predict wind speed

Cited by 25 publications

References 46 publications

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model

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