Influence of Land Use information over performance when predicting spatiotemporal electricity load demand

In a world where the growing concern about climate change becomes more apparent, electricity industry has its role to play in slowing down such an evolution. In fact, tools and services could be provided to consumers in order to better manage their ever-increasing consumption. But, while waiting for such a complex solution to be deployed, a first step would be to better forecast current consumption. Such an improvement would enable to generate electricity accordingly, while renewable solutions are not presently optimal – i.e., intermittent, long-term storage issue, etc.Electricity Load Demand (ELD) fluctuations do not solely depend on temporal factors, they also have a spatial contribution that is often underestimated. Indeed, it is possible to map different ELD profiles with their Land Use (LU) information. For example, ELD in residential areas fits with residents’ commuting style, while in industrial areas, it mostly correlates with working hours. This paper aims to investigate such a relation. Considering ELD data on a 500m square grid and LU data on a 100m square grid, different 500m cell labeling has been defined. In order to assess if cells with similar labels have similar ELD, they have been grouped under the same forecast model. Several types of models (Multilayer Perceptron, Recurrent Neural Network) have been used to compare their performance and efficiency. This study confirms that, all models considered, some labels are more difficult to forecast than others. Such associations can reduce by over 30% the prediction error compare to a per cell scenario. Additional investigations would be needed to further reduce prediction error and to help models better seize the land specificity of each grid-cell. External data also affects ELD, and pairing them with an optimal LU labeling could be a promising solution.