Generalised Regression Hypothesis Induction for Energy Consumption Forecasting Rueda Delgado, Ramón Pegalajar Cuéllar, Manuel Molina Solana, Miguel José Guo, Yi-Ke Pegalajar Jiménez, María Del Carmen Symbolic regression Energy consumption Forecasting Pattern recognition This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series. 2020-05-06T12:29:14Z 2020-05-06T12:29:14Z 2019-03-20 journal article Rueda, R.; Cuéllar, M.P.; Molina-Solana, M.; Guo, Y.; Pegalajar, M.C. Generalised Regression Hypothesis Induction for Energy Consumption Forecasting. Energies 2019, 12, 1069. [doi:10.3390/en12061069] http://hdl.handle.net/10481/61857 10.3390/en12061069 eng EC/H2020/743623 http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España MDPI