Quantifying the environmental impacts of student behavior uncertainty in campus power system: an electricity load forecasting-driven carbon reduction method

Abstract

Accurate power load forecasting is a prerequisite for optimizing energy allocation, aligning campus electricity supply with demand, and mitigating carbon emissions in campus integrated energy systems. To improve campus load forecasting accuracy and support low-carbon energy dispatch, this study proposes an IRDOA-DSR-Transformer-based carbon reduction framework for campus Integrated Energy System (IES). First, input features related to uncertain student behaviors, including campus electric vehicle charging records, temporal characteristics, and environmental variables, are compressed using Uniform Manifold Approximation and Projection (UMAP). The dimensionality reduction process preserves environmentally sensitive data structures while filtering redundant noise, thereby enhancing feature quality for subsequent forecasting and carbon-reduction analysis. Second, a dynamic sparse routing gate mechanism is introduced to adaptively emphasize critical electricity-use periods, especially environmentally sensitive intervals such as overnight charging. This mechanism strengthens the model’s capability to capture multi-scale temporal dependencies and mitigates the limitations of conventional Transformers in identifying key load patterns, reducing dispatch inefficiencies and emission increases caused by forecasting errors. Finally, an improved Inheritance Reorganization Dhole Optimization Algorithm is used to optimize the model’s hyperparameters. Experimental results show that the proposed method achieves a maximum goodness-of-fit coefficient of 98.41% and reduces Root Mean Square Error (RMSE) by up to 50% relative to benchmark models. Under identical dispatch logic, the method lowers campus carbon emissions by approximately 3.9%–7.3%, demonstrating both forecasting advantages and practical carbon-reduction potential.