Abstract
This study investigates the application of short-term load forecasting (STLF), which consists of estimating a future demand within a period of time up to one week, to thermal unit commitment problems (TUCP), providing schedule for power plant operations. Both problems have fundamental importance for power system operations and good results on STLF may also influence TUCP performance. The pattern similarity approach is chosen for STLF, which allows the use of regression algorithms based on machine learning applied to time series analysis and forecasting results are used as information for generators scheduling. This study proposes a framework containing these tasks with a deep review of them and provides some statistical information regarding the performance and validation of the framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anjos, M.F., Conejo, A.J.: Unit commitment in electric energy systems. Found. Trends Electr. Energy Syst. 1(4), 220–310 (2017)
Dudek, G.: Pattern similarity-based methods for short-term load forecasting—Part 1: principles. Appl. Soft Comput. 37, 277–287 (2015)
Dudek, G.: Pattern similarity-based methods for short-term load forecasting—Part 2: models. Appl. Soft Comput. 36, 422–441 (2015)
Dudek, G.: Neural networks for pattern-based short-term load forecasting: a comparative study. Neurocomputing 205, 64–74 (2016)
Girgis, A.A., Varadan, S.: Unit commitment using load forecasting based on artificial neural networks. Electr. Pow. Syst. Res. 32(3), 213–217 (1995)
Greenwood, P.E., Nikulin, M.S.: A Guide to Chi-squared Testing. Wiley Series in Probability and Statistics. Wiley, New York (1996)
Gross, G., Galiana, F.D.: Short-term load forecasting. Proc. IEEE 75(12), 1558–1573 (1987)
Hobbs, W.J., Hermon, G., Warner, S., Shelbe, G.B.: An enhanced dynamic programming approach for unit commitment. IEEE Trans. Power Syst. 3(3), 1201–1205 (1988)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecasting 22(4), 679–688 (2006)
ISO-NE Homepage. http://www.iso-ne.com/
Kamboj, V.K., Bath, S.K.: A solution to energy and environmental problems of electric power system using hybrid harmony search-random search optimization algorithm. Cogent Eng. 3(1), 1–29 (2016)
Karthikeyan, S.P., Palanisamy, K., Rani, C., Raglend, I.J., Kothari, D.P.: Security constrained unit commitment problem with operational, power flow and environmental constraints. WSEAS Trans. Power Syst. 4(4), 53–56 (2009)
Krishnamurthy, D., Li, W., Tesfatsion, L.: An 8-Zone test system based on ISO New England data: development and application. IEEE Trans. Power Syst. 31(1), 234–246 (2016)
Lang, K., Zhang, M., Yuan, Y.: Improved neural networks with random weights for short-term load forecasting. PLOS ONE 10(12), e0143175 (2015)
Li, C.-A., Johnson, R.B., Svoboda, A.J.: A new unit commitment method. IEEE Trans. Power Syst. 12(1), 113–119 (1997)
Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and building. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015)
Song, C., Zheng, Y.: The delicate analysis of short-term load forecasting. IOP Conf. Ser. Mater. Sci. Eng. 199(1), 012097 (2017)
Taylor, J.W., McSharry, P.E.: Short-term load forecasting methods: an evaluation based on european data. IEEE Trans. Power Syst. 22(4), 2213–2219 (2007)
Wei, S., Mohan, L.: Application of improved artificial neural networks in short-term power load forecasting J. Renew. Sustain. Energy, 7(4) (2015)
Weron, R.: Modeling and Forecasting Electricity Loads and Prices. Wiley, Chichester (2006)
Willmott, C., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30, 79–82 (2005)
Zor, K., Timur, O., Çelik, Ö., Yildirim, H.B., Teke, A.: Interpretation of error calculation methods in the context of energy forecasting. In: 12th Conference on Sustainable Development of Energy, Water and Environment Systems, Dubrovnik, Croatia, vol. 722, pp. 1–9 (2017)
Acknowledgements
The authors acknowledge the support of CNPq, FAPEMIG and CAPES in this study. Special thanks to Vladimir Stanojevic for his DP implementation in MATLAB and to prof. Frederico Coelho (DELT-UFMG) for explanations regarding some statistical tests.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, G.C., Lisboa, A.C., Vieira, D.A.G., Saldanha, R.R. (2019). Application of Load Forecasting i Thermal Unit Commitment Problems: A Pattern Similarity Approach. In: Valenzuela, O., Rojas, F., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2018. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-26036-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-26036-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26035-4
Online ISBN: 978-3-030-26036-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)