A novel methodology for day-ahead buildings energy demand forecasting to provide flexibility services in energy markets

In future smart grid environment, local energy markets will become a reality to provide flexibility. Consequently, it will be essential not only to implement accurate energy consumption forecasters at the building level to determine which buildings can provide the required flexibility, but also at an aggregated level to anticipate power system boundary conditions. Thus, both forecasters play a key role in supporting the reliable and secure operation of smart grids and developing future demand response strategies. Although there is a piece of literature that addressed energy demand forecasting for day-ahead horizons, proposed algorithms only focused on improving accuracy neglecting energy markets technical boundary conditions. This study presents a novel methodology based on random forest machine learning algorithm to predict day-ahead energy demand at individual buildings with a 15-minute resolution. Furthermore, an analysis has been conducted to assess whether the application of time-series decomposition techniques or shape factors can enhance the accuracy of the proposed methodology. The results indicate that the proposed methodology is effective and accurate, exhibiting a MAPE of 10.77% – 31.52% and an R² of 0.51–0.70 for individual buildings. These findings demonstrate the potential of the methodology for future energy markets.