Short-term load forecasting for smart water and gas grids: A comparative evaluation

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@InProceedings{Fagiani:2015:ieeeEEEIC,

• author = "Marco Fagiani and Stefano Squartini and Roberto Bonfigli and Francesco Piazza",
• booktitle = "15th IEEE International Conference on Environment and Electrical Engineering (EEEIC)",
• title = "Short-term load forecasting for smart water and gas grids: A comparative evaluation",
• year = "2015",
• pages = "1198--1203",
• abstract = "Moving from a recent publication of Fagiani et al. [1], short-term predictions of water and natural gas consumption are performed exploiting state-of-the-art techniques. Specifically, for two datasets, the performance of Support Vector Regression (SVR), Extreme Learning Machine (ELM), Genetic Programming (GP), Artificial Neural Networks (ANNs), Echo State Networks (ESNs), and Deep Belief Networks (DBNs) are compared adopting common evaluation criteria. Concerning the datasets, the Almanac of Minutely Power Dataset (AMPds) is used to compute predictions with domestic consumption, 2 year of recordings, and to perform further evaluations with the available heterogeneous data, such as energy and temperature. Whereas, predictions of building consumption are performed with the datasets recorded at the Department for International Development (DFID). In addition, the results achieved for the previous release of the AMPds, 1 year of recordings, are also reported, in order to evaluate the impact of seasonality in forecasting performance. Finally, the achieved results validate the suitability of ANN, SVR and ELM approaches for prediction applications in small-grid scenario. Specifically, for the domestic consumption the best performance are achieved by SVR and ANN, for natural gas and water, respectively. Whereas, the ANN shows the best results for both water and natural gas forecasting in building scenario.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/EEEIC.2015.7165339",
• month = jun,
• notes = "Also known as \cite{7165339}",

}

Genetic Programming entries for Marco Fagiani Stefano Squartini Roberto Bonfigli Francesco Piazza

Citations