Generalised Regression Hypothesis Induction for Energy Consumption Forecasting
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AuthorRueda Delgado, Ramón; Pegalajar Cuéllar, Manuel; Molina Solana, Miguel José; Guo, Yi-Ke; Pegalajar Jiménez, María Del Carmen
Symbolic regressionEnergy consumptionForecastingPattern recognition
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]
SponsorshipThis work was supported by the Spanish Government (research project TIN201564776-C3-1-R). M. Molina-Solana was funded by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 743623.
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.