An ANN-Based Energy Forecasting Framework for the District Level Smart Grids
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.
KeywordsANN District energy management Grid electricity Smart city
- 11.Fanti, M.P., Mangini, A.M., Roccotelli, M., Ukovich, W., Pizzuti, S.: A control strategy for district energy managemet. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, pp. 432–437 (2015)Google Scholar
- 15.Valerio, A., Giuseppe, M., Gianluca, G., Alessandro, Q., Borean, C.: Intelligent systems for energy prosumer buildings at district level. In: 23rd International Conference on Electricity Distribution (CIRED), pp. 1–5 (2015)Google Scholar
- 16.ISSDA, CER Smart Metering Project. http://www.ucd.ie/issda/data/commissionforenergyregulationcer/. Accessed 15 Feb 2016