Generalised Regression Hypothesis Induction for Energy Consumption Forecasting

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...

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Bibliographic Details
Published in:Energies (Basel) 2019-03, Vol.12 (6), p.1069
Main Authors: Rueda, R., Cuéllar, M., Molina-Solana, M., Guo, Y., Pegalajar, M.
Format: Article
Language:English
Subjects:
Anomalies
Artificial intelligence
Datasets
Energy consumption
Energy management
Forecasting
Genetic algorithms
Multiple objective analysis
Neural networks
Parameterization
pattern recognition
symbolic regression
Time series
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12061069