Water & Natural Gas Forecasting
The goal of the load forecasting is to provide a correct estimate of the future consumption. It is possible to classify the forecasting in terms of prediction horizon and target dimension. In the former category can be identified short, medium, and long-term prediction; whereas, in the latter one can be discerned several scenarios, the domestic, building, and city are the commons ones.
In Water and Natural Gas fields the lack of suitable databases/datasets acted as a serious bottleneck for the development of innovative Computational Intelligence and Machine Learning methods, that have been widely adopted in the Energy field.
Marco Fagiani, Stefano Squartini, Roberto Bonfigli and Francesco Piazza, "Short-term Load Forecasting for Smart Water and Gas Grids: a comparative evaluation" in Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on, June 2015, to appear.
Marco Fagiani, Stefano Squartini, Leonardo Gabrielli, Susanna Spinsante, and Francesco Piazza, "A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas and Water Grids: Analysis and Experiments," Neurocomputing, Special Issue, 2015, to appear.
Marco Fagiani, Stefano Squartini, Leonardo Gabrielli, Susanna Spinsante, Francesco Piazza, "Domestic Water and Natural Gas Demand Forecasting by using Heterogeneous Data: A Preliminary Study" in: WIRN2014: The 24th Italian Workshop on Neural Networks, 2014, to appear.
Marco Fagiani, Stefano Squartini, Leonardo Gabrielli, Mirco Pizzichini, and Susanna Spinsante, "Computational Intelligence in Smart water and gas grids: An up-to-date overview" in Neural Networks (IJCNN), 2014 International Joint Conference on, July 2014, pp. 921–926.
Water & Natural Gas Leakage Detection
Marco Fagiani, Stefano Squartini, Marco Severini, and Francesco Piazza, "A Novelty Detection approach to identify the occurrence of leakage in Smart Gas and Water Grids" in Neural Networks (IJCNN), 2015 International Joint Conference on, July 2015, to appear.