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.
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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.
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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
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