Cost-Oriented Load Forecasting

TL;DR

This paper introduces a cost-oriented load forecasting framework that uses a differentiable loss function aligned with real power system costs, improving prediction relevance over traditional MSE-based methods.

Contribution

It develops a novel differentiable loss function reflecting actual costs and integrates it into load forecasting models, validated through case studies on IEEE and GEFCom2012 datasets.

Findings

Cost-oriented loss function better reflects real system costs.

Improved load forecasting accuracy in case studies.

Enhanced decision-making in power system operations.

Abstract

Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012 (GEFCom2012).