Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
An introduction to variable and feature selection
The Journal of Machine Learning Research
Methodology for long-term prediction of time series
Neurocomputing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Mutual information and k-nearest neighbors approximator for time series prediction
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
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Many preprocessing and prediction techniques have been used for large-scale electricity load forecasting. However, small-scale prediction, such as in the case of public buildings, has received little attention. This field presents certain specific features. The most distinctive one is that consumption is extremely influenced by the activity in the building. For that reason, a suitable approach to predict the next 24-hour consumption profiles is presented in this paper. First, the features that influence the consumption are processed and selected. These environmental variables are used to cluster the consumption profiles in subsets of similar behavior using neural gas. A direct forecasting approach based on Support Vector Regression (SVR) is applied to each cluster to enhance the prediction. The input vector is selected from a set of past values. The approach is validated on teaching and research buildings at the University of León.