Abstract
With the evolution of smart grids in recent years, load forecasting has received more research focus than ever before. Several techniques, especially based on artificial neural network and support vector regression, have been proposed for this purpose. However, due to lack of appropriate modeling of external influences over the load data, the performance of these techniques remarkably deteriorates while making forecast for the peak load values, especially on short-term basis. In this paper, we present a strategy to forecast hourly peak load using Recurrent Neural Network with Long-Short-Term-Memory architecture. The novelty lies here in improving the forecast accuracy by an intelligent incorporation of available domain knowledge during the forecast process. Experimentation is carried out to forecast hourly peak load in five different zones in USA. The experimental results are found to be encouraging.
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Patel, A., Das, M., Ghosh, S.K. (2021). Short-Term Load Forecasting: An Intelligent Approach Based on Recurrent Neural Network. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_6
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DOI: https://doi.org/10.1007/978-3-030-49336-3_6
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