Integrating the Expected Future: Schedule Based Energy Forecasting

Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors, as these outliers are particularly challenging to manage during operation. Incorporating planning information - such as known data about users' future behavior or scheduled events - has the potential to significantly enhance the accuracy and specificity of forecasts. Although there have been attempts to integrate such expected future behavior, these efforts consistently rely on conventional regression models to process this information. These models often lack the flexibility and capability to effectively incorporate both dynamic, forward-looking contextual inputs and historical data. To address this challenge, we conceptualize this combined forecasting and regression challenge as a sequence-to-sequence modeling problem and demonstrate, with three distinct models, that our contextually enhanced transformer models excel in this task. By leveraging schedule-based contextual information from the Swiss railway traction network, our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption. Specifically, enhancing the transformer models with contextual information resulted in an average reduction of mean absolute error by 40.6%, whereas other state-of-the-art methods did not demonstrate any significant improvement. Despite extensive research efforts to forecast energy usage in electrical grids, operators still encounter significant outliers when faced with unexpected scenarios, with relative errors occasionally exceeding 50%, posing considerable operational challenges. Our research reveals that a critical limitation of current forecasting approaches is their over-reliance on trends and periodic patterns from past observations. We challenge this conventional focus on historical data as the primary source for energy forecasts and advocate for integrating contextual information about the expected future, such as anticipated user behavior and scheduled events. By incorporating this expected future information, we significantly improve the accuracy and specificity of load forecasts. This approach proves crucial for improving forecasting capabilities, and this methodology can be broadly applied to other domains where similar planning information is available. Electrical energy distinguishes itself from other traded commodities because its transmission follows the power flow equations Kundur (2012); Pagnier & Chertkov (2021). These equations require that the amount of energy consumed (demand) must always match the amount of energy produced (supply) to maintain the stability of the power grid Ullah et al. (2021). In compliance with these laws, grid operators collaborate closely with energy traders to ensure frequency synchronization and voltage stabilization, preventing damage to infrastructure and grid-connected assets Klyuev et al. (2022).