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Support Vector Regression for Electricity Consumption Prediction in a Building in Japan

Abstract : This work studies how to apply support vector machines in order to forecast the energy consumption of buildings. Usually, support vector regression is implemented using the sequential minimal optimisation algorithm. In this work, an alternative version of that algorithm is used to reduce the execution time. Several experiments were carried out taking into account data measured during one year. The weather conditions were used as independent variables and the consumed amount of electricity was considered as the parameter to predict. The model has been trained using the first six months of the dataset whereas it was validated using the following three months and tested taking into account the last three months of measurements. From obtained results, a good performance of the model is observed. © 2016 IEEE.
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Frédéric Magoulès, Michel Piliougine, David Elizondo. Support Vector Regression for Electricity Consumption Prediction in a Building in Japan. 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016, Aug 2016, Paris, France. pp.189-196, ⟨10.1109/CSE-EUC-DCABES.2016.184⟩. ⟨hal-02426679⟩



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