Firefly Algorithm-Based Semi-Supervised Learning with Transformer Method for Shore Power Load Forecasting

Abstract

Load forecasting of shore power (SP) plays an important role in the power decision-making of the electrical grid due to docked ships are necessary to plug into the electrical grid. However, obtaining a large amount of labeled data on docked ships is time-consuming, presenting a challenge for Shore Power Load Forecasting. Additionally, multiple raw information entries for docked ships could lead to feature redundancy. To address these issues, we proposed a novel three-stage load forecasting method which includes attributive feature selection, semi-supervised learning (SSL) method for the mean of load distribution prediction, and a transformer-based model for variance prediction. Firstly, Firefly Algorithm (FA) is adopted to extract representative attribute features of docked ships to deal with the feature redundancy. Next, the selected feature set and label set are divided into two parts: a few labeled data and a large amount of labeled data. And we propose a Π-model-based SSL method to predict the load distribution. Finally, we propose a transformer-based model to predict the variance of load distribution. Our model takes into account all historical load data of each docked ship for context learning. Further, we consider that the attribute features would also affect the variance prediction, so the latent features of the Π-model are served as the initial condition which concatenates historical load data. We evaluated our model using 328 power load data from various ships that berth at Zhenjiang Port with shore power, totaling approximately 21,521 hours. The experiments prove the accuracy and efficiency of our proposed model, producing promising forecasting results.