Algorithm selection and combining multiple learners for residential energy prediction - Güngör, Onat; Akşanlı, Barış; Aydoğan, Reyhan (Future generation computer systems, 2019)
Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly. •Energy prediction is important for utilities to balance supply and demand.•A single prediction algorithm might not perform well for a variety of households.•In our data set, we see that TESLA outperforms other time series prediction methods.•Algorithm selection performs better than a single prediction algorithm.•Random Forest as our algorithm selection method gives the minimum prediction error.•Combining multiple learners further increases energy prediction accuracy.