Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism

TL;DR

This paper presents a dynamic, node-specific demand forecasting method that leverages cost asymmetry and feedback to optimize financial outcomes, achieving significant savings by adapting to local conditions.

Contribution

It introduces a novel demand forecasting approach that incorporates cost asymmetry and feedback mechanisms for improved financial optimization at the node level.

Findings

Achieved $5.1M annual savings.

Demonstrated adaptability to station-specific conditions.

Effectively incorporates unmodeled factors like macroeconomic shifts.

Abstract

This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least expensive scenario. Savings are calculated and a self-regulation mechanism modulates the adjustments magnitude based on the observed savings, enabling the model to adapt to station-specific conditions and unmodeled factors such as calibration errors or shifting macroeconomic dynamics. Finally, empirical results demonstrate the model's ability to achieve \$5.1M annual savings.