In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Rueda:2019:10.3390/en12061069,
author = {Rueda, R and Cuéllar, M and Molina-Solana, M and Guo, Y and Pegalajar, M},
doi = {10.3390/en12061069},
journal = {Energies},
pages = {1069--1069},
title = {Generalised regression hypothesis induction for energy consumption forecasting},
url = {http://dx.doi.org/10.3390/en12061069},
volume = {12},
year = {2019}
}
TY - JOUR
AB - 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.
AU - Rueda,R
AU - Cuéllar,M
AU - Molina-Solana,M
AU - Guo,Y
AU - Pegalajar,M
DO - 10.3390/en12061069
EP - 1069
PY - 2019///
SN - 1996-1073
SP - 1069
TI - Generalised regression hypothesis induction for energy consumption forecasting
T2 - Energies
UR - http://dx.doi.org/10.3390/en12061069
UR - http://hdl.handle.net/10044/1/67867
VL - 12
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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