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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.

Part 3: MAKE Privacy; International audience; Due to the significant increase of the global electricity demand and the rising number of urban population, the electric consumption in a city has attracted more attentions. Given the fact that public buildings occupy a large proportion of the electric consumption, the accurate prediction of electric consumptions for them is crucial to the rational electricity allocation and supply. This paper studies the possibility of utilizing urban multi-source data such as POI, pedestrian volume etc. to predict buildings’ electric consumptions. Among the multiple datasets, the key influencing factors are extracted to forecast the buildings’ electric power demands by the given probabilistic graphical algorithm named EMG. Our methodology is applied to display the relationships between the factors and forecast the daily electric power demands of nine public buildings including hotels, shopping malls, and office buildings in city of Hangzhou, China over the period of a month. The computational experiments are conducted and the result favors our approach.

The accurate prediction approach of urban buildings’ electricity consumption is an important foundation for smart urban energy management. It provides the decision basis for electricity deployment at peak times. This paper presents a knowledge graph of urban building electricity consumption called ECKG. It provides an effective way to obtain the influencing factors of buildings’ electricity consumption. In addition, in order to improve the accuracy of prediction, this paper proposes the logarithmic electricity consumption gravity model and error correction model based on data selection. We use 17520 hours’ electricity consumption of a five-star hotel building in Shanghai, China as the study case, and 8 common models as benchmarks to conduct the comparisons. Our approach outperforms all benchmarks in terms of average accuracy.