Regresar

Forecasting Energy Consumption in Residential Department Using Convolutional Neural Networks

Abstract:

During 2017, the construction and operation of buildings worldwide represented more than a third (36%) of the final energy used and 40% of the carbon dioxide emissions. Hence, in the last decade, there has been great interest in analyzing the energy efficiency in buildings from different approaches. In this paper, black-box approaches based on artificial neural networks to pbkp_redict the energy consumption of a selected residential department building are proposed. The potential of convolutional neural networks (CNN) applied to images and videos is tested in time series as one-dimensional (1D) sequences. CNN models and other combinations with Long Short-Term Memory (LSTM) such as CNN-LSTM and ConvLSTM are proposed to make pbkp_redictions in two scenarios, i.e., for pbkp_redicting energy consumption in the next 24 h and 7 days. The results showed that the best model was CNN for the first scenario, and in the second scenario, CNN-LSTM performed better. These models can be very useful in pbkp_redictive control systems considered in buildings to foresee with great precision the energy consumption behavior in the short, medium, and long term.