Value-Oriented Data-Driven Approach for Electrical Load Forecasting Apt to Facilitate Vehicle-to-Grid Scheduling
Jiahao Zhong, Xiang Lei, Ziyun Shao, Linni Jian
Abstract
The purpose of this article is to propose a value-oriented electrical load forecasting (ELF) approach that aims to minimize load variance by leveraging vehicle-to-grid (V2G) technology. To achieve this, it is critical and urgent to design a loss function that can accurately measure the suboptimality of decisions induced by forecast errors, especially since the commonly used mean squared error (MSE) falls short in this regard. The Lagrange multiplier method and Karush–Kuhn–Tucker conditions are initially used to elucidate the impact of forecast errors on the actual charging power of electric vehicles. Building on this foundation, a differentiable loss function called mean relative magnitude error (MRME) is put forward for value-oriented ELF, which allows parameter updates in the long short-term memory (LSTM) model via the gradient descent method during the training stage. Furthermore, the MRME and MSE loss functions are combined using a weighted sum method to leverage their respective advantages. Numerous case studies have demonstrated that the LSTM model, whether using MRME alone or in combination with MSE, achieves superior and more robust V2G scheduling performance compared to using MSE alone. The weight coefficients for combining MRME and MSE loss functions are also discussed.