Rescheduling costs and adaptive asymmetric errors guided closed-loop prediction of power loads in mine integrated energy systems

The development of an integrated energy system for mining that efficiently recycles multiple resources is a crucial strategy for achieving dual carbon reduction targets in the mining sector. Precise load forecasting is fundamental to ensuring the safe and efficient scheduling of this system. However, existing studies often overlook the coupling between load forecasting and scheduling results, treating them independently, which frequently leads to high rescheduling costs due to forecasting errors. To address this issue, we propose a closed-loop load forecasting algorithm that incorporates rescheduling costs and asymmetric errors. We first proposed a data generation and model construction strategy by using real load, predicted load, and rescheduling costs to capture the relationship between load forecasting and rescheduling costs. Considering the different impacts of under-forecasting and over-forecasting on scheduling results, the rescheduling cost model is further integrated with asymmetric prediction errors to define the loss function of the Bi-LSTM based forecasting model. Additionally, an optimization strategy for self-tuning asymmetric prediction error fusion coefficients is designed to ensure the accuracy of load forecasting. The proposed algorithm is applied to the power load forecasting of an integrated energy system in a coal mine in Shanxi. The results demonstrate the effectiveness of the algorithm in reducing system rescheduling costs while ensuring forecasting accuracy, highlighting its potential application in power load forecasting for mine integrated energy systems.