Power prediction through energy consumption pattern recognition for smart buildings

In this paper, we propose a Non-negative Mixture of Experts (NME) model for smart buildings that is capable of making accurate power forecasting by recognizing characteristic consumption patterns. The model uses prediction error as a metric to guide the feature learning process subject to non-negativity constraints. The objective is to understand and model energy consumption behaviors in commercial buildings at the appliance level so as to facilitate dynamic pricing and demand response. Application of the NME model to a large dataset of device power measurements results in the discovery of meaningful energy usage patterns that are characteristic of the working and idle states of the building space, with the additional advantage that the learned features also optimize the energy prediction model. The model can be learned by stochastic gradient descent, which is suitable for large-scale problems, and an online version is also suggested.