Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time dependencies and proposes a novel energy load forecasting methodology based on sample generation and Sequence-to-Sequence (S2S) deep learning algorithm. The S2S architecture that is commonly used for language translation was adapted for energy load forecasting. Experiments focus on Gated Recurrent Unit (GRU) based S2S models and Long Short-Term Memory (LSTM) based S2S models. All models were trained and tested on one building-level electrical consumption dataset, with five-minute incremental data. Results showed that, on average, the GRU S2S models outperformed LSTM S2S, RNN S2S, and Deep Neural Network models, for short, medium, and long-term forecasting lengths.