Forecasting the Short-Term Electricity Consumption of Building Using a Novel Ensemble Model

Abstract

The accurate prediction approach of urban buildings' electricity consumption is an important foundation for smart urban energy management. It provides a decision basis for reasonable electricity deployments upon different scenarios. Usually, a single model cannot solve linear and nonlinear problems that may occur in electricity consumption prediction effectively and may produce predictions with unsatisfactory accuracy and stability. Moreover, some prediction models are also poorly interpretable and generalized, which makes them difficult to be applied in practice. To overcome these problems, this paper proposes an ensemble prediction model called gravity gated recurrent unit electricity consumption model which integrates the gated recurrent unit model and the proposed logarithmic electricity consumption gravity model. The weights are derived from average mutual information and weighted entropy. We use two years (17 520 hours) electricity consumption of a five-star hotel building in Shanghai, China, as the study case to illustrate our approach, and apply nine common prediction models as the benchmarks to conduct the computational experiments and comparisons. Furthermore, we also employ the electricity consumption data of another type of building (office building) to evaluate the generalization capability of the proposed ensemble model. Our approach outperforms all benchmarks in terms of accuracy, stability, and generalization.

Keywords

• Data analysis
• prediction algorithms
• electricity consumption
• ensemble model
• information theory