Energy Forecasting with Building Characteristics Analysis

With the installation of smart meters, high resolution building-level energy consumption data become increasingly accessible, which not only provides more accurate data for energy forecasting at the aggregated level but also enables datadriven energy forecasting for individual buildings. On the one hand, individual buildings exhibit high randomness, making the forecasting problem at the building-level more challenging. On the other hand, buildings usually have their own characteristics, therefore such valuable information needs to be considered in the forecast models at the aggregation level. In this paper we investigate how unique characteristics of buildings could affect the performance of forecasting models and aim to identify defining patterns of buildings. The usefulness of the proposed approach is demonstrated using data from three real-world buildings.