An Initial Study on Load Forecasting Considering Economic Factors

TL;DR

This study introduces a novel load forecasting approach that incorporates economic factors by using a new objective function and quantile regression, demonstrating improved cost reduction over traditional methods.

Contribution

It proposes a new objective function and QR algorithm tailored for load forecasting that accounts for economic impacts of forecasting errors.

Findings

The proposed method reduces economic costs more effectively.

It outperforms traditional time series, neural network, and SVM methods.

Demonstrated on New England load data.

Abstract

This paper proposes a new objective function and quantile regression (QR) algorithm for load forecasting (LF). In LF, the positive forecasting errors often have different economic impact from the negative forecasting errors. Considering this difference, a new objective function is proposed to put different prices on the positive and negative forecasting errors. QR is used to find the optimal solution of the proposed objective function. Using normalized net energy load of New England network, the proposed method is compared with a time series method, the artificial neural network method, and the support vector machine method. The simulation results show that the proposed method is more effective in reducing the economic cost of the LF errors than the other three methods.