Abstract
Short-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering algorithm to identify load patterns and to the COMB distance to capture differences between time series. Clusters’ labels are then used to identify similar sequences of days. Temperature information is also considered in the day-ahead load forecasting, resorting to the K-Nearest Neighbor approach. CSPF algorithm is intended to provide the aggregate forecast of Portugal's national load, for the next day, with a 15-min discretization, based on data from the Portuguese Transport Network Operator (TSO). CSPF forecasting performance, as evaluated by RMSE, MAE and MAPE metrics, outperforms three alternative/baseline methods, suggesting that the proposed approach is promising in similar applications.
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References
Azadeh A, Ghaderi SF, Sheikhalishahi M, Nokhandan BP (2014) Optimization of short load forecasting in electricity market of Iran Using artificial neural networks. Optim Eng 15:485–508. https://doi.org/10.1007/s11081-012-9200-8
Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern, Part B: Cybernetics 28(3):301–315. https://doi.org/10.1109/3477.678624
Bokde N, Asencio-Cortes G, Martinez-Alvarez F., Bokde MN (2016) Package ‘PSF’. URL: https://cran.r-project.org/web/packages/PSF/PSF.pdf. Accessed 13 April 2021
Caiado J, Crato N, Peña D (2006) A periodogram-based metric for time series classification. Comput Stat Data Anal 50(10):2668–2684. https://doi.org/10.1016/j.csda.2005.04.012
Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods 3(1):1–27
Cardoso, MGMS, Martins, A (in press) The performance of a combined distance between time series. In Recent Developments in Statistics and Data Science - Proceedings of the XXV Congress of the Portuguese Statistical Society. Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R. and de Carvalho, M. (eds.), Springer
Cardoso MGMS, Martins A, Lagarto J (2021) Combining various dissimilarity measures for clustering electricity market prices. In Estatística: Desafios Transversais às Ciências dos Dados - Atas do XXIV Congresso da Sociedade Portuguesa de Estatística (Paula Milheiro et al. eds), Edições SPE
Charlton N, Singleton C (2014) A refined parametric model for short term load forecasting. Int J Forecast 30:364–368. https://doi.org/10.1016/j.ijforecast.2013.07.003
Chen Y, Luh PB, Guan C, Zhao Y, Michel LD, Coolbeth MA, Friedland PB, Rourke SJ (2010) Short-term load forecasting: Similar day-based wavelet neural networks. IEEE Trans Power Syst 25(1):322–330. https://doi.org/10.1109/TPWRS.2009.2030426
Cheng C-H, Wei L-Y (2010) One step-ahead ANFIS time series model for forecasting electricity loads. Optim Eng 11:303–317. https://doi.org/10.1007/s11081-009-9091-5
Dedinec A, Filiposka S, Dedinec A, Kocarev L (2016) Deep belief network based electricity load forecasting: An analysis of Macedonian case. Energy 115:1688–1700. https://doi.org/10.1016/j.energy.2016.07.090
Duch W (2000) Similarity-based methods: a general framework for classification, approximation and association. Control Cybernetics 29(4):937–968
Fallah SN, Ganjkhani M, Shamshirband S, Chau KW (2019) Computational intelligence on short-term load forecasting: A methodological overview. Energies 12(3):393. https://doi.org/10.3390/en12030393
Fan S, Chen L (2006) Short-Term Load Forecasting Based on an Adaptive Hybrid Method. IEEE Trans Power Syst 7(1):392–401. https://doi.org/10.1109/TPWRS.2005.860944
Fan S, Hyndman R (2012) Short-term load forecasting based on a semi-parametric additive model. IEEE Trans Power Syst 7:1–8. https://doi.org/10.1109/TPWRS.2011.2162082
Gaillard P, Goude Y, Nedellec R (2016) Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. Int J Forecast 32:1038–1050. https://doi.org/10.1016/j.ijforecast.2015.12.001
Goude Y, Nedellec R, Kong N (2014) Local Short and Middle Term Electricity Load Forecasting with Semi-Parametric Additive Models. IEEE Trans on Smart Grid 5(1):440–446. https://doi.org/10.1109/TSG.2013.2278425
Hennig C (2020) Package ‘fpc’. cran. r-project. org/web/packages/fpc/fpc. Pdf. Accessed 13 April 2021
Heydari A, Nezhad MM, Pirshayan E, Garcia DA, Keynia F, Santoli L (2020) Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl Energy 277:115503. https://doi.org/10.1016/j.apenergy.2020.115503
Hong T (2010) Short Term Electric Load Forecasting. PhD Thesis. North Carolina State University
Hong T, Shahidehpour M (2015) Load Forecasting Case Study. EISPC. https://pubs.naruc.org/pub.cfm?id=536E10A7-2354-D714-5191-A8AAFE45D626. Accessed 13 April 2021
Hong T, Fan S (2016) Probabilistic electric load forecasting: A tutorial review. Int J Forecast 32:914–938. https://doi.org/10.1016/j.ijforecast.2015.11.011
Hyndman RJ, & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 8 April 2022.
Iglesias F, Kastner W (2013) Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6(2):579–597. https://doi.org/10.3390/en6020579
Ilic S, Selakov A, Vukmirovic S, Erdeljan A, Kulic F (2013) Short-term load forecasting in large scale electrical utility using artificial neural networks. J Sci Ind Res 72:739–745
Jin CH, Pok G, Lee Y, Park HW, Kim KD, Yun U, Ryu KH (2015) A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting. Energy Convers Manag 90:84–92. https://doi.org/10.1016/j.enconman.2014.11.010
Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons. https://doi.org/10.1002/9780470316801
Kuster C, Rezgui Y, Mourshed M (2017) Electrical load forecasting models: A critical systematic review. Sustain Cities Soc 35:257–270. https://doi.org/10.1016/j.scs.2017.08.009
Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K, Studer M (2013) Package ‘cluster’. URL: https://cran.r-project.org/web/packages/cluster/cluster.pdf. Accessed 13 April 2021
Mandal P, Senjyu T, Urasaki N, Funabashi T (2006) A neural network based several-hour-ahead electric load forecasting using similar days approach. Int J Elect Power & Energy Syst 28(6):367–373. https://doi.org/10.1016/j.ijepes.2005.12.007
Martinez-Alvarez F, Troncoso A, Riquelme JC, Ruiz JSA (2010) Energy time series forecasting based on pattern sequence similarity. IEEE Trans Knowledge and Data Eng 23(8):1230–1243. https://doi.org/10.1109/TKDE.2010.227
Metaxiotis K, Kagiannas A, Askounis D, Psarras J (2003) Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Convers Manag 44:1525–1534. https://doi.org/10.1016/S0196-8904(02)00148-6
Mohandes M (2002) Support vector machines for short-term electrical load forecasting. Int J Energy Res 26:335–345. https://doi.org/10.1002/er.787
Montero P, Vilar JA (2014) TSclust: An R Package for Time Series Clustering. J Stat Softw. https://doi.org/10.18637/jss.v062.i01
Mu Q, Wu Y, Pan X, Huang L, Li X (2010) Short-term load forecasting using improved similar days method. In Proceedings of 2010 Asia-Pacific Power and Energy Engineering Conference, Chengdu. https://doi.org/10.1109/APPEEC .2010.5448655
Rodrigues P (2008) Hierarchical clustering of time-series data streams. IEEE Trans Knowledge Data Eng 20(5):1–13. https://doi.org/10.1109/TKDE.2007.190727
Ružic S, Vuckovic A, Nikolic N (2003) Weather Sensitive Method for Short Term Load Forecasting in Electric Power Utility of Serbia. IEEE Trans Power Syst 18(4):1581–1586. https://doi.org/10.1109/TPWRS.2003.811172
Sharifzadeh M, Sikinioti-Locka A, Shah N (2019) Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression. Renew Sustain Energy Rev 108:513–538. https://doi.org/10.1016/j.rser.2019.03.040
Siegel, S, Castellan, Jr (1988) Nonparametric Statistics for the Behavioral Sciences, 2nd edition, McGraw-Hill.
Wang P, Liu B, Hong T (2016) Electric load forecasting with recency effect: A big data approach. Int J Forecast 32:585–597. https://doi.org/10.1016/j.ijforecast.2015.09.006
Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10(8):1168. https://doi.org/10.3390/en10081168
Acknowledgements
This work was supported by Instituto Politécnico Lisboa (IPL) with reference IPL/2020/ELForcast_ISEL and Fundação para a Ciência e a Tecnologia, grants UIDB/00315/2020 and UIDB/50021/2020.
We thank Tiago G. S. Chambel Cardoso for the paper Figures design.
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Martins, A., Lagarto, J., Canacsinh, H. et al. Short-term load forecasting using time series clustering. Optim Eng 23, 2293–2314 (2022). https://doi.org/10.1007/s11081-022-09760-1
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DOI: https://doi.org/10.1007/s11081-022-09760-1