Trend-based time series data clustering for wind speed forecasting

Kushwah, Varsha; Wadhvani, Rajesh; Kushwah, Anil Kumar

doi:10.1177/0309524x20941180

Cited by 12 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forecasting time series has several applications in different domains, many of them are instrumented using deep learning models (Brahma and Wadhvani, 2020a), (Kushwah et al, 2021), (Tran et al, 2019), (Gharehbaghi and Linden, 2018). Wind speed forecasting models for 15 years are reviewed by Sheela and Deepa (2013), concluding that the models of neural networks outperform non-neural network models.…”

Section: Introductionmentioning

confidence: 99%

Adaptive wind data normalization to improve the performance of forecasting models

et al. 2022

Self Cite

View full text Add to dashboard Cite

Wind speed forecasting, a time series problem, plays a vital role in estimating annual wind energy production in wind farms. Calculation of wind energy helps to maintain stability between electricity production and consumption. Deep learning models are used for predicting time series data. However, as wind speed is non-stationary and irregular, pre-processing of these data is necessary to get accurate results. In this paper, static normalization techniques like min–max, z-score, and adaptive normalization are used for pre-processing wind datasets, and further, their forecasting results are compared. Adaptive normalization increases the learning rate and gives better forecasting results than static normalization. The RMSE value was reduced by 9.18% for the NREL dataset when adaptive normalization was used instead of z-score normalization and by 23.58% for the Weather dataset. The datasets used are taken from National Renewable Energy Laboratory (NREL) and Kaggle’s Dataset.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive wind data normalization to improve the performance of forecasting models

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The grouping focuses on the patterns and seasonality of time series data values for each instance. Based on the trend and seasonality to recognize the same kind of structure of the instances for the grouping (Kushwah et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A novel time series data clustering approach for wind speed forecasting

2022

Self Cite

View full text Add to dashboard Cite

Wind energy plays an essential role in the generation process of sustainable energy, with a bright future. Therefore, predicting wind speed fluctuations and their output power plays a crucial role in electric power generation. The integration of wind power is based on the accuracy of wind speed and power prediction model. In this paper, a clustering algorithm is proposed based on the length of the trendlet components. After spotting the different clusters, one suitable cluster is selected for modeling using the panda’s correlation method. This paper uses specific ARIMA, Naive Forecast, and Holt Winter models to forecast the selected cluster. Here three hybrid models, namely, C-ARIMA, C-NAIVE Forecast, and C-Holt-Winter, are proposed for wind speed forecasting. The performances of the proposed models are evaluated using the mean absolute error (MAE) and root mean squared error (RMSE). The experiment outcomes show that the cluster-based forecasting technique (Hybrid models) improved performance compared with un-clustered forecasting techniques.

show abstract

“…The results indicate that the hybrid version had better accuracy than the unbiased versions of ARIMA and ANN. Kushwah et al (2020) suggested a hybrid model consisting of statistical models and trend-based clustering. Compared to statistical models used alone, the hybrid model performs better.…”

Section: Introductionmentioning

confidence: 99%

Discrete wavelet transforms based hybrid approach to forecast wind speed time series

Kushwah

Wadhvani

2021

Wind Engineering

Self Cite

View full text Add to dashboard Cite

The wind resources have been estimated by using physical models, statistical models, and artificial intelligence models. Wind power calculation helps us measure the annual energy that will sustain the balance between electricity generation and electricity consumption. Wind speed plays a significant role in calculating wind power, due to which here we focus on wind speed prediction. In this paper, hybrid models for wind speed forecasting have been proposed. The hybrid models are formed by combining the time series decomposition technique, that is, discrete wavelet transform (DWT), with statistical models, that is, autoregressive integrated moving average (ARIMA) and generalized autoregressive score (GAS), respectively. These hybrid models are referred to as DWT-ARIMA and DWT-GAS. DWT decomposes the original series into sub-series. After that, statistical models are applied to each sub-series for prediction. In the end, aggregate the prediction results of each sub-series to get the final forecasted series. For experimentation purposes, statistical and hybrid models are applied to various datasets that are taken from the NREL repository. In our studies, the hybrid version demonstrates better results in terms of accuracy and complexity, which indicates superior performance in most cases compared to the existing statistical models.

show abstract

Trend-based time series data clustering for wind speed forecasting

Cited by 12 publications

References 18 publications

Adaptive wind data normalization to improve the performance of forecasting models

Adaptive wind data normalization to improve the performance of forecasting models

A novel time series data clustering approach for wind speed forecasting

Discrete wavelet transforms based hybrid approach to forecast wind speed time series

Contact Info

Product

Resources

About