A novel time series data clustering approach for wind speed forecasting

Abstract

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.

Keywords

Wind time series ARIMA model Naive forecast model Holt Winter model hybrid model

Get full access to this article

View all access options for this article.

References

Bermúdez

Segura

Vercher

(2006) Improving demand forecasting accuracy using nonlinear programming software. Journal of the Operational Research Society 57(1): 94–100.

Burtiev

Greenwell

Kolivenko

(2013) Time series analysis of wind speed and temperature in Tiraspol, Moldova. Environmental Engineering & Management Journal 12(1): 23–33.

Fulcher

Little

Jones

(2013) Highly comparative time-series analysis: The empirical structure of time series and their methods. Journal of The Royal Society Interface 10(83): 20130048.

Garcke

Iza-Teran

Marks

, et al. (2017) Dimensionality reduction for the analysis of time series data from wind turbines. In: Griebel

Schüller

Schweitzer

(eds) Scientific Computing and Algorithms in Industrial Simulations. Berlin, Heildeberg: Springer, pp.317–339.

Goodwin

(2014) Using naïve forecasts to assess limits to forecast accuracy and the quality of fit of forecasts to time series data. Available at: https://ssrn.com/abstract=2515072 (accessed 26 October 2014).

Rajopadhye

Ghalia

Wang

, et al. (1999) Forecasting uncertain hotel room demand. Information Sciences 132(1–4): 1–11.

Inniss

(2006) Seasonal clustering technique for time series data. European Journal of Operational Research 175(1): 376–384.

Johnpaul

Prasad

Nickolas

, et al. (2020) Trendlets: A novel probabilistic representational structures for clustering the time series data. Expert Systems with Applications 145: 113–119.

Kotsialos

Papageorgiou

Poulimenos

(2005) Long-term sales forecasting using holt-winters and neural network methods. Journal of Forecasting 24(5): 353–368.

10.

Kushwah

Wadhvani

Kushwah

(2021) Trend-based time series data clustering for wind speed forecasting. Wind Engineering 45: 992–1001.

11.

Kuznetsov

Mohri

(2020) Discrepancy-based theory and algorithms for forecasting non-stationary time series. Annals of Mathematics and Artificial Intelligence 88: 367–399.

12.

Längkvist

Karlsson

Loutfi

(2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters 42: 11–24.

13.

Maatallah

Achuthan

Janoyan

, et al. (2015) Recursive wind speed forecasting based on hammerstein auto-regressive model. Applied Energy 145: 191–197.

14.

Syakur

Khotimah

Rochman

EMS

, et al. (2018) Integration k-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conference Series Materials Science and Engineering336: 012–017.

15.

Vilar

Lafuente-Rego

D’Urso

(2018) Quantile autocovariances: A powerful tool for hard and soft partitional clustering of time series. Fuzzy Sets and Systems 340: 38–72.

16.

Wang

Gattone

2nd Harris

, et al. (2005) Effectiveness of vasopressin v2 receptor antagonists opc-31260 and opc-41061 on polycystic kidney disease development in the pck rat. Journal of the American Society of Nephrology 16(4): 846–851.

17.

Yang

Sharma

, et al. (2015) Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy 81: 111–119.

18.

Zhang

(2020) Short-term wind power forecasting approach based on seq2seq model using NWP data. Energy 213: 118371.

19.

Zhu

Zhang

Wen

, et al. (2019) Fast and stable clustering analysis based on grid-mapping k-means algorithm and new clustering validity index. Neurocomputing 363: 149–170.