Abstract
In this paper a preliminary study concerning prediction of domestic consumptions of water and natural gas based on genetic programming (GP) and its combination with extended Kalman filter (EKF) is presented. The used database (AMPds) are composed of power, water, natural gas consumptions and temperatures. The study aims to investigate novel solutions and adopts state-of-the-art approaches to forecast resource demands using heterogeneous data of an household scenario. In order to have a better insight of the prediction performance and properly evaluate possible correlation between the various data types, the GP approach has been applied varying the combination of input data, the time resolution, the number of previous data used for the prediction (lags) and the maximum depth of the tree. The best performance for both water and natural gas prediction have been achieved using the results obtained by the GP model created for a time resolution of 24 h, and using a set of input data composed of both water and natural gas consumptions. The results confirm the presence of a strong correlation between natural gas and water consumptions. Additional experiments have been executed in order to evaluate the effect of the prediction performance using long period heterogeneous data, obtained from the U.S. Energy Information Administration (E.I.A.).
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Fagiani, M., Squartini, S., Gabrielli, L., Spinsante, S., Piazza, F. (2015). Domestic Water and Natural Gas Demand Forecasting by Using Heterogeneous Data: A Preliminary Study. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_18
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DOI: https://doi.org/10.1007/978-3-319-18164-6_18
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