Integrating the Expected Future: Schedule Based Energy Forecasting

• URL: http://arxiv.org/abs/2409.05884v1
• Date: Mon, 26 Aug 2024 12:32:40 GMT
• Title: Integrating the Expected Future: Schedule Based Energy Forecasting
• Authors: Raffael Theiler, Olga Fink,
• Abstract summary: Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors. incorporating planning information has the potential to significantly enhance the accuracy and specificity of forecasts. Our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption.
• Score: 6.675805308519987
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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