Abstract
This study presents an Artificial Neural Network (ANN) based district level smart grid forecasting framework for predicting both aggregated and disaggregated electricity demand from consumers, developed for use in a low-voltage smart electricity grid. To generate the proposed framework, several experimental study have been conducted to determine the best performing ANN. The framework was tested on a micro grid, comprising six buildings with different occupancy patterns. Results suggested an average percentage accuracy of about 96%, illustrating the suitability of the framework for implementation.
Keywords
B. Yuce—The work has been funded by the European Commission in the context of the MAS2TERING project (the grant number 619682).
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The authors would like to acknowledge the financial support of the European Commission in the context of the MAS2TERING project (Ref: 619682) funded under the ICT-2013.6.1 - Smart Energy Grids program.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yuce, B., Mourshed, M., Rezgui, Y. (2017). An ANN-Based Energy Forecasting Framework for the District Level Smart Grids. In: Hu, J., Leung, V., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds) Smart Grid Inspired Future Technologies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-47729-9_12
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DOI: https://doi.org/10.1007/978-3-319-47729-9_12
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